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Individuals who are injured in the workplace typically have a greater risk of delayed return to work (RTW) and other poor health outcomes compared to those not injured at work. It is not known whether these differences hold true for mild traumatic brain injuries (MTBI). The present study examined differences associated with workplace and non-workplace MTBI upon intake to a specialty MTBI clinic, their outcomes, and risk factors that influence RTW. Slow-to-recover participants were recruited from consecutive referrals to four outpatient MTBI clinics from March 2015 to February 2017. Two clinics treat Worker’s Compensation claimants and two clinics serve patients with non-work related injuries in the publically funded health care system. Of 273 eligible patients, 102 completed an initial study assessment (M age = 41.2 years, SD age = 11.7; 54% women) at an average of 2–3 months post injury. Participants were interviewed about their MTBI and completed a battery of standardized questionnaires and performance validity testing. Outcomes, including RTW, were assessed via telephone follow-up 4–5 months later. Workplace injuries comprised 45.1% of the sample. The workplace MTBI group had a greater proportion of men and lower education levels compared to the non-workplace MTBI group. The two groups had a comparable post-concussion symptom burden and performance validity test failure rate. Workplace MTBI was associated with greater post-traumatic stress symptoms. Fifteen patients (14.7%) were lost to follow-up. There were no workplace/non-workplace MTBI differences in RTW outcome at 6–7 months post injury. Of the entire sample, 42.5% of patients had full RTW, 18.4% had partial RTW, and 39.1% had no RTW. Greater post-concussion symptom burden was most predictive of no RTW at follow-up. There was no evidence that the workplace and non-workplace MTBI groups had different risk factors associated with prolonged work absence. Despite systemic differences in compensation and health care access, the workplace and non-workplace MTBI groups were similar at clinic intake and indistinguishable at follow-up, 6–7 months post injury.
Citation: Terry DP, Iverson GL, Panenka W, Colantonio A, Silverberg ND (2018) Workplace and non-workplace mild traumatic brain injuries in an outpatient clinic sample: A case-control study. PLoS ONE 13(6): e0198128. https://doi.org/10.1371/journal.pone.0198128
Editor: Fernando de Castro, Instituto Cajal-CSIC, SPAIN
Received: November 15, 2017; Accepted: May 14, 2018; Published: June 1, 2018
Copyright: © 2018 Terry et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information file.
Funding: Funding for this study was provided by WorkSafeBC, through a Specific Priorities Research Grant (#RS2014-SP03). The funder had no role in the study design, data collection, data analysis, decision to publish, or preparation of the manuscript. NDS received salary support from a Clinician-Scientist Career Development Award from the Vancouver Coastal Health Research Institute and a Health Professional Investigator Award from the Michael Smith Foundation for Health Research. The authors acknowledge unrestricted philanthropic support from the Heinz Family Foundation.
Competing interests: Grant Iverson has a clinical practice in forensic neuropsychology involving individuals who have sustained mild TBIs. He acknowledges unrestricted philanthropic support from the Mooney-Reed Charitable Foundation and ImPACT Applications, Inc. Noah Silverberg has a clinical practice in forensic neuropsychology and William Panenka has a clinical practice in forensic neuropsychiatry involving individuals who have sustained mild TBIs. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Approximately 1 in 4 mild traumatic brain injuries (MTBIs) in adults occur at work[1,2]. Across the spectrum of severity, workplace brain injuries are associated with substantial productivity loss, economic burden[3–5], persistent symptoms and occupational disability[1,6]. Considering traumatic injuries of all kinds, patients who are injured at their workplace are at greater risk of delayed return to work (RTW) and other poor health outcomes than those who were injured outside of work. It is not known whether this difference holds for MTBI and which factors account for worse outcomes from workplace injuries. There are several reasons to expect differences between workplace and non-workplace MTBIs. For example, whereas many falls and recreational accidents are non-compensable, patients who are injured at work are legally entitled to compensation. Compensation access is highly predictive of delayed RTW[9,10]. Worker’s Compensation claimants in Canada have substantial rates of suspected symptom exaggeration after MTBI[11,12]. However, compensation access cannot fully account for differences in RTW rates. For example, in New Zealand’s universal entitlement system, where workplace and non-workplace injures afford the same access to compensation, workplace injuries are associated with a greater risk of delayed RTW, as well as long-term physical and emotional health problems.
Differences in patient demographics might contribute to differences between workplace and non-workplace injuries. MTBI is more common in men, and this gender gap is wider for workplace injuries (including MTBI)[2,4,14,15], but women are at greater risk for chronic symptoms following MTBI[16–18]. Women more often access health services than men in some settings, but not for a workplace MTBI. Gender and workplace factors may also interact. Traditionally masculine work environments may be less supportive for returning injured workers. MTBI occurs disproportionately in manufacturing, construction, and transportation[2,4,6,15]. These occupations are associated with lower education attainment and decision-making latitude at work, factors that have been previously linked to worse outcomes from MTBI[21–24].
Another factor that may contribute to poor prognosis and delayed RTW after work injury is blame attribution[8,25,26]: who or what the patient perceives is at fault for their injury. Those who are injured at work are most likely to blame equipment or others in the workplace rather than themselves, whereas self-blame is most common in non-workplace injuries. Blame perception regarding work injury is predictive of disability at 6-months post injury. The broader concept of injustice may be more predictive of occupational disability than simply asking injured workers who is at fault[27–30]. In relation to an injury or illness, perceived injustice is a set of beliefs that comprise blame, a sense of unfairness, severity of loss, and that their loss is irreparable. Injured workers who blame their employer may be less inclined to return to that work setting because they feel unsafe. Further, those with a workplace MTBI may face relationship strain related to the increased work burden on co-workers in their absence or feeling retaliated against by their employer/company.
Mental health and interpersonal/psychosocial difficulties may also be barriers to returning to work. Posttraumatic stress disorder (PTSD) is a relatively frequent (12–27%) comorbidity that complicates recovery from MTBI. Even symptoms of post-traumatic stress that are not severe enough to meet full diagnostic criteria magnify the risk of not returning to work at 6–9 months post MTBI. PTSD may be especially common and problematic following workplace MTBI, because workplace injuries in general are associated with more PTSD symptoms than non-workplace injuries and PTSD symptoms, in turn, are associated with delayed RTW. They may be a particular barrier for returning to the same job with the same employer, because that would by definition involve exposing oneself to trauma-related triggers.
The primary aim of this study was to compare symptomatic workplace vs. non-workplace patients with MTBIs with respect to their initial presentation to a specialty MTBI clinic and RTW outcomes. A secondary aim was to determine if different risk factors influence RTW in each group. Compared to the non-workplace MTBI group, we predicted that the workplace MTBI group would include a greater proportion of men, have lower education levels, a lower proportion of occupations with high decision-making latitude, stronger perceived injustice beliefs, worse PTSD symptoms, higher rate of symptom invalidity, and lower rate of RTW. We further hypothesized that perceived injustice and traumatic stress symptoms would be more strongly related to RTW status at follow-up in the workplace MTBI group.
Materials and methods
Participants and procedures
Participants were recruited via consecutive referrals to four outpatient clinics in the Vancouver (Canada) area that specialize in MTBI/concussion rehabilitation from March 2015 to February 2017. Two of these clinics treat Worker’s Compensation claimants, while the other two serve patients with non-work related injuries, in the publicly funded health care system. Participants with work-related MTBIs were referred to clinic by their Worker’s Compensation Board case managers. Participants whose MTBI was not work-related could have been referred by their family physician or specialist, or self-referred. Referrals to these speciality MTBI clinics were initiated because of persistent symptoms or risk factors for persistent symptoms. Patients referred to these clinics are usually on an atypical recovery trajectory and they are considered at risk for long-term symptoms and problems. To be eligible for the study, participants had to be 18–65 years old, have sustained a MTBI within the past six months (based on the World Health Organization Neurotrauma Task Force definition), be fluent in English, and employed prior to the injury. The University of British Columbia Ethics Board, the Vancouver Coastal Health Research Institute, and the Fraser Health Research Institute approved this study. Participation in this research study was voluntary. The measures described below were administered to patients as part of a research battery (i.e., not in usual clinical care). Of the 273 patients who agreed to be contacted for research purposes during the recruitment period, 93 were excluded based on an initial screening phone call (i.e., 44 did not meet MBTI criteria; 26 were injured > 6 months prior; 16 were not employed prior to the injury; 4 were not fluent in English; 3 were > 65 years old) and 40 declined to participate. 140 participants were invited for an initial assessment, but 38 could not be scheduled or did not attend. There were 102 participants who gave written consent for this study and completed the initial study assessment.
Participants were assessed at two time points, when they initially presented to clinic and again 4–5 months following their initial study visit, after undergoing rehabilitation. The initial study assessment occurred 2–26 weeks post injury (M = 12.05; SD = 6.26) and included a standardized semi-structured interview of their demographics (e.g., education, occupation), prior health history (e.g., pre-injury mental health treatment), and MTBI [e.g., self-reported injury characteristics, such as mechanism of injury, loss of consciousness (LOC), amnesia, confusion/disorientation, co-occurring orthopaedic injury, healthcare utilization, compensation-seeking status]. Based on the participant’s report of their injury, LOC was classified into one of the following mutually exclusive categories: witnessed (i.e., participant learned from another person’s eye witness account that they had an LOC), unwitnessed (i.e., participant inferred that they had an LOC but were not observed by another person to be unresponsive), unknown LOC (i.e., participant stated that they were unsure whether they had an LOC or not), or denied LOC. Medical records were not accessed for the purpose of this study. Further, participants completed several questionnaires (see Measures). The follow-up assessment occurred 21–47 weeks post injury (M = 31.57; SD = 6.05) via telephone. During this follow-up assessment, participants participated in a structured interview about their RTW status. Their answers were classified into three possible outcomes to form our primary outcome variable: (i) Full RTW: Returned to same hours and responsibilities at their pre-injury job, or started a new job with comparable hours/responsibilities; (ii) Partial RTW: Returned to pre-injury job but with reduced hours or responsibilities, or started a new job that is less demanding; or (iii) No RTW: Not working because of the MTBI, other injuries from the same event, or unrelated to injury event. Additionally, at the time of the follow-up assessment, a treating clinician at each site completed a health service utilization summary for each patient (6 summaries missing; 3 workplace MTBI and 3 non-workplace MTBI).
Injustice Experience Questionnaire (IEQ).
The IEQ is a 12-item questionnaire that assesses perceived injustice associated with injury. This is operationalized as a set of cognitions regarding how individuals view their injury and its consequences, including beliefs about being treated unfairly and suffering unnecessarily as a result of another’s actions. Individual items are rated on a 0–4 scale based on frequency (i.e., “never” to “always”) and summed for the total score (range: 0–48). This scale correlates with symptom chronicity and occupational disability in a variety of health conditions[27,28,30,35,36]. Internal consistency was excellent for this scale in the present sample (Cronbach’s α = .92).
PTSD checklist for DSM-5 (PCL-5).
The PCL-5 is a 20-item questionnaire that assesses symptoms of post-traumatic stress over the past month. Participants rate how much they were bothered by each symptom (0/“not at all” to 4/“extremely”). Total scores range from 0–80. This measure has high reliability and good evidence of convergent and discriminant validity[37,38]. Internal consistency was high for this scale in the present sample (Cronbach’s α = .94).
Brief Pain Questionnaire (BPQ).
The BPQ is a 5-item questionnaire that assesses current pain in the following five bodily regions: the head/skull, neck, chest/abdomen/back, arms/shoulders, and pelvis/legs. Current pain intensity is rated from 0 (none) to 3 (severe) in each bodily region, with a possible score range of 0–15. Internal consistency was acceptable at the initial (Cronbach’s α = .75) and follow-up assessments (Cronbach’s α = .79).
Medical Symptom Validity Test (MSVT).
Compensation-seeking status is strongly associated with outcome from MTBI, including RTW[9,39]. People who are involved in compensation claims are more likely to report persistent/exaggerated symptoms and have poor effort during neuropsychological testing. Performance validity testing is a term used for purpose-built tests or embedded validity indicators in a neuropsychological evaluation. Performance validity test failure is independently associated with both compensation-seeking (vs. ineligible for compensation) and workplace (vs. non-workplace) MTBI. The MSVT is sensitive to motivational factors but insensitive to the effects of genuine memory impairment and TBI.[44,45] We used evidence-based cut-off scores on the three “easy” subtests (Immediate Recognition, Delayed Recognition, and Consistency) to identify cases with probable below-capacity performance. The MSVT has been validated for use in patients with TBI and compares favorably with legacy instruments[46–49].
At the initial study assessment, participants were asked a single question about how they expected their recovery would progress. Similar self-prognosis ratings have been shown to have prognostic value in both MTBI[50–52] and mixed traumatic injury samples. Participants selected whether they thought their symptoms would: “get better soon,” “get better slowly,” “never get better,” or “don’t know.” “Never get better” was a rarely endorsed response (n = 1), so this data point was excluded from analyses.
British Columbia Postconcussion Symptom Inventory (BC-PSI).
The BC-PSI is a 16-item self-report questionnaire that measures the frequency and severity of postconcussion symptoms. Item scores are derived from the ratings on frequency and severity scores. The total BC-PSI score is the sum of the item scores (range: 0–52). This scale’s reliability and validity have been well-established[54–56]. In our sample, internal consistency was high at the initial study assessment (Cronbach’s α = .96) and at follow-up (Cronbach’s α = .96). Similar to our previous studies, patients were classified as meeting International Classification of Diseases, 10th Revision (ICD-10) criteria for mild postconcussional syndrome (Mild PCS) if they reported symptoms as being mild or worse (i.e., item scores ≥2) in at least three symptom categories (i.e., physical symptoms, cognitive symptoms, emotional symptoms, or sleep-related symptoms)[57,58]. Moderate PCS (i.e., item scores ≥3 greater across three symptom categories) was calculated similarly.
World Health Organization Disability Assessment Schedule 2.0 (WHODAS)-12 item.
The WHODAS is a brief, cross-cultural and disease non-specific standardized disability instrument. It measures disability in six domains and is sensitive to a variety of medical and psychiatric conditions, including TBI[60–64]. We recently reported that this measure has desirable psychometric characteristics in MTBI, with a sample that overlaps with the present study. Patients select how much difficulty they have experienced over the past 30 days (1/“none” to 5/“extreme or cannot do”, score range: 12–60). The structured interview format was administered in this study. In our sample, internal consistency was high (Cronbach’s α = .91).
Lam Employment Absence and Productivity Scale.
The LEAPS is a questionnaire that assesses work difficulties and absenteeism. On item 4, parts a-g, patients report how much they were bothered by problems like low energy, poor concentration, anxiety, and making mistakes while at work over the past two weeks (0/“none of the time, 0%” to 4/“all of the time, 100%”; score range: 0–28). A total score of 6 or higher indicates some degree of work impairment. In our sample, internal consistency was acceptable for this measure (Cronbach’s α = .78). As in a prior study, we used the LEAPS to add granularity to RTW outcome classifications.
Independent samples t-tests were used to evaluate group differences on continuous variables between the workplace and non-workplace MTBI groups. Analyses of covariance (i.e., ANCOVAs) were used to compare the groups on measures with continuous variables (i.e., age, PCL-5, IEQ, BC-PSI, WHODAS, number of primary care visits) while statistically controlling for other continuous variables (i.e., weeks from injury to assessment) that may influence this relationship. A small number of patients omitted a single item on the BC-PSI (n = 1) and PCL-5 (n = 5). We imputed these missing values by averaging the participant’s responses to the remainder of the items from that scale and rounding the average item value to the nearest whole number. In so doing, a total of 0.07% (6 of 8,304) questionnaire items were imputed. We used chi-squared tests to compare the groups on categorical variables. All comparisons used a statistical significance level of p<.05.
Logistic regression was used to examine whether the prognostic value of previously established and novel candidate risk factors for delayed RTW after MTBI differed for participants with workplace vs. non-workplace MTBI. “Any RTW” (i.e., partial and full RTW) vs No RTW was the dichotomous outcome. Post-concussion symptom reporting[23,51], co-occurring orthopedic injuries[23,62], and self-rated prognosis[51,53] have been significant predictors in prior studies and so were included as predictors in our logistic regression analyses. Self-rated prognosis was treated categorically, with “get better soon” serving as the reference group. Traumatic stress (PCL-5) was included as a predictor because of preliminary evidence of its prognostic utility after MTBI for RTW and prolonged symptoms. Further, probable below-capacity performance (i.e., MSVT failure) was included as a predictor because lower PVT scores have been predictive of longer wage replacement benefits (a proxy for RTW) following MTBI. In contrast, variables such as sex and age have not contributed to the prediction of RTW in most prior studies and were omitted from our model[23,51]. We included perceived injustice (IEQ) as a novel predictor of RTW because of its association with poor outcomes and disability in other medical populations[27,30,35] and because it may have an especially potent role in work-related injuries. Injury setting (i.e., workplace vs. non-workplace) was not included in the model because it was not related to RTW outcome in preliminary analyses (see Results). However, we were interested in potential interactions between injury setting and our other variables of interest, with the rationale that some variables (e.g., traumatic stress, perceived injustice) may be more salient and impactful for those injured at work. When introducing this interaction term, we also included the number of weeks between injury and the initial study assessment as a predictor in our analyses to control for the possible bias introduced because the workplace and non-workplace groups were not well-matched on the timing of initial study assessment. Predictors were entered into the logistic regression model in two steps. First, all candidate predictors were entered simultaneously (BC-PSI, PCL, IEQ, MSVT failure, co-occurring orthopedic injury, and self-rated prognosis). Second, the number of weeks between injury and the initial assessment and a single interaction term were added. The second step was repeated for each interaction term (workplace vs. non-workplace group x a predictor entered in the first step), one at time.
Demographics and injury characteristics are presented in Table 1. Workplace injuries comprised 45.1% of the final sample (n = 46). Participants with a workplace MTBI were somewhat younger than those who sustained a non-workplace MTBI, but this difference was not significant [t(98) = 1.71, p = .09]. The workplace MTBI group had a greater proportion of men [Χ2(1) = 7.38, p = .007] and they were less likely to have been educated beyond high school [56.5% vs. 80.4%, Χ2(1) = 6.78, p = .009]. Occupation differed between workplace and non-workplace MTBI [Χ2(6) = 19.90 p = .003], as did mechanism of injury [Χ2(5) = 32.71, p<.001]. Table 1 provides a breakdown of occupational categories and mechanisms of injury by group. There were no differences in rates of loss of consciousness, amnesia, and confusion/disorientation between workplace and non-workplace MTBI (ps>.05). The two groups also had a similar rate of a co-occurring orthopedic injury [Χ2(1) = 1.95, p = .16] and pre-injury mental health treatment [Χ2(1) = 1.52, p = .22].
The workplace MTBI group had a shorter duration between their injury and the initial study assessment compared to the non-workplace MTBI group [t(99) = 2.92, p = .004, Cohen’s d = -0.60]. That is, they present for outpatient speciality MTBI care sooner. Time since injury was therefore used as a covariate in comparisons between groups on non-categorical variables (i.e., ANCOVA). Participants had seen their primary care physician an average of 6.4 times (SD = 4.4, Md = 5, IQR = 5–8) before the initial study assessment; there was no significant difference in the number of post-MTBI primary care visits between the workplace (M = 5.9, Md = 5) and non-workplace (M = 6.8, Md = 6) groups (Mann-Whitney U = 1120.0, p = .19). Additional self-reported healthcare utilization between the injury and initial study assessment can be seen in Table 1.
As expected, virtually all participants with workplace MTBI were off work and receiving administrative (wage loss) benefits at the time of the initial assessment. That is what would have prompted their referral for specialized MTBI treatment. Participants with non-workplace MTBI were more likely to have already returned to work in some capacity [Χ2(3) = 11.16, p = .011] by the initial assessment and were more likely to be seeking compensation through different means [Χ2(2) = 31.72, p<.001], such as personal injury litigation (see Table 1). In the initial assessment, the workplace MTBI group had more symptoms of traumatic stress [PCL-5, F(1, 98) = 4.04, p = .047, adjusted d = 0.42] when covarying for weeks since injury. There were no differences between workplace and non-workplace MTBI groups on perceived injustice [IEQ, F(1, 98) = 2.89, p = .09], overall post-concussion symptom severity (BC-PSI total score, F(1, 98) = 1.39, p = .24), or pain [BPQ, F(1, 97) = 2.75, p = .10] when covarying for weeks since injury. There were no group differences on MSVT failure rate [Χ2(1) = 0.43, p = .51] or recovery expectations [Self Prognosis; Χ2(2) = 2.81, p = .25].
Of the 102 participants who completed the initial assessment at clinic intake, 15 (14.7%) were lost to follow-up. Participants who were lost to follow-up did not differ from those who were retained with respect to age, sex, education (i.e., secondary vs. not), or race/ethnicity (Caucasian vs. non-Caucasian; ps>.05). A similar proportion of patients were lost to follow-up in the workplace and non-workplace MTBI groups [Χ2(1) = 0.48, p = .49]. The workplace and non-workplace MTBI groups did not differ on time between the injury and follow-up assessment [t(84) = 0.43, p = .67]. Similar to the initial assessment, the workplace and non-workplace MTBI groups differed in compensation status at follow-up [Χ2(3) = 25.71, p<.001].
Access to treatment is inherently different for patients inside vs. outside of the Worker’s Compensation system. Participants who sustained a non-workplace MTBI typically received only a group education session (100%), with a minority (<10%) receiving additional services in that clinic (e.g., physiatry consultation). Many of these participants reported accessing additional services in the community. In contrast, participants who sustained a workplace MTBI commonly received a brief neuropsychological assessment (100%), medical examination (85.7%), multi-day interdisciplinary assessment (80.9%), multi-week rehabilitation program (68.6%), gradual RTW transition support (57.1%), and a job site visit (40.0%).
Outcomes obtained from the follow-up telephone assessment are shown in Table 2. At follow-up, there were no differences in RTW status between the workplace and non-workplace MTBI groups [Χ2(2) = 0.16, p = .92; Cramer’s V = 0.04]. Collapsing those with full RTW and partial RTW into a single “any RTW” group did not alter the results. At follow-up, there were no group differences in post-concussion symptoms (BC-PSI, t(84) = -0.10, p = .92, d = 0.07), functional disability (WHODAS, t(85) = 0.55, p = .59, d = -0.12), or pain (BPQ, t(85) = -0.28, p = .78, d = 0.08) between groups. Similar proportions of both groups were operationalized as having mild ICD-10 postconcussional syndrome [Χ2(1) = 0.23, p = .63; V = 0.05] and moderate ICD-10 postconcussional syndrome [Χ2(1) = 0.35, p = .56; V = 0.06]. Using a more conservative definition of complete RTW (i.e., full RTW and LEAPS score 0–5) did not reveal group differences. A similar proportion of participants in the workplace and non-workplace MTBI groups fully returned to work with normal productivity [Χ2(1) = 0.02, p = .89].
As shown in Table 3, greater perceived injustice, greater post-concussion symptoms, and answering “don’t know” when symptoms would resolve (compared to “get better soon”) were significantly related to delayed RTW in unadjusted (single predictor) regression models. The logistic regression with all predictor variables (step 1) was well calibrated [Hosmer and Lemeshow Χ2(7) = 5.58, p = .59] and statistically significant [Χ2(7) = 22.41, p = .002]. Prediction accuracy was fair (Nagelkerke R2 = .31; area under the receiver operating curve = .78, p<.001). In this model, post-concussion symptoms and traumatic stress symptoms were statistically significant, such that greater post-concussion symptoms and less traumatic symptoms were associated with not working at follow-up. These results did not change in Step 2, when time between injury and initial assessment were entered into the model. Further, none of the ‘injury setting-by-predictor’ interaction terms were statistically significant, indicating that none of the candidate risk factors were differentially related to RTW outcome in the workplace vs. non-workplace groups.
There are important differences between the pre-injury characteristics, mechanisms of injury, health care services, and recovery trajectories between people who sustain an MTBI in sport, military, and civilian trauma settings[68,69]. Prior studies examining traumatic injuries of all kinds have reported that people who are injured in workplace accidents are at elevated risk for poor outcomes[7,70]. The primary aim of the present study was to compare patients with workplace vs. non-workplace MTBI. We studied patients who were referred to specialty care because of atypical recovery following MTBI. Almost all participants met ICD-10 symptom criteria for postconcussional syndrome during the study period, and their symptom reporting was likely influenced by factors separate from neurotrauma (such as traumatic stress, persistent bodily pain, and exaggeration). When interpreting the results of this study, it is important to appreciate that the sample was specialty clinic-referred, and generally slow-to-recover, highly symptomatic, with disproportionately high rates of risk factors for poor outcome. We hypothesized that patients injured at work would be a distinct subgroup, with more adverse risk profiles and lower rates of RTW compared to patients who were injured in other settings. We expected that our findings would support a stratification that may help explain the heterogeneity of MTBI outcomes. Overall, our study did not demonstrate important differences between workplace and non-workplace related MTBIs in these specialty clinic samples.
We found similarities and differences between patients with persistent symptoms following workplace vs. non-workplace MTBI. The two groups similarly accessed primary care during the acute post-injury period, but patients with workplace MTBI presented for outpatient speciality care a few weeks faster, on average. This difference is not surprising because those with a Worker’s Compensation claim are assigned a case manager whose role it is monitor the worker’s recovery and to initiate referrals for treatment. For people who sustain an MTBI in other settings, navigating health care services are generally left to the patient themselves and/or their primary care provider. Consistent with the broader literature on musculoskeletal injuries[21–24], patients with workplace MTBI were more likely to be male blue-collar workers without a Bachelor’s or graduate degree. Falls were a common mechanism of injury in both groups, but patients with workplace injuries most frequently sustained their MTBI by being struck with an object, whereas motor vehicle accidents were the most frequent cause of non-workplace MTBI.
At clinic intake (2–3 months post injury), there were no significant group differences in post-concussion symptoms, perceived injustice, or expectations for recovery. The only difference was that workplace MTBI was associated with greater PTSD symptoms. This could, in part, be related to the differences in mechanisms of injuries (e.g., more assaults, fewer sport injuries). Despite differences in compensation access (patients with Worker’s Compensation Claims receive administrative benefits but cannot sue whereas patients with non-workplace injuries may not have access to administrative benefits but often can seek compensation through personal injury litigation), the rate of suspected exaggeration on neuropsychological testing (performance validity test failure) was similar and fairly high in both groups (20–25%).
The workplace and non-workplace MTBI groups were indistinguishable at follow-up, 6–7 months post injury. A very similar number had returned to work in a partial (18–19%) or full (37–41%) capacity. They also reported a similar burden of residual post-concussion symptoms (on the BC-PSI), functional disability (on the WHODAS), and pain (on the BPQ). An absence of differences in post-acute outcome is in keeping with the finding that workplace MTBI was associated with both favorable (male sex) and unfavorable (low education and job decision-making latitude) prognostic factors, and similar psychosocial profiles to participants with non-workplace MTBI at clinic intake, with the exception of more PTSD symptoms. However, the absence of differences in post-acute outcome might be surprising when considering that patients with workplace MTBI received more comprehensive and coordinated rehabilitation services. It is difficult to draw conclusions about why these two workplace and non-workplace MTBI groups ended up looking similar at follow-up. It may be that the workplace MTBI group was at greater risk of poor outcome due to unmeasured variables, and the timely and extensive rehabilitation they received offset that increased risk. It is also possible that both groups were on a similarly poor recovery trajectory, and the health services they received did not substantially influence their trajectories. We entertained that the workplace and non-workplace MTBI groups may have achieved similar outcomes via different pathways, and so also sought to examine whether certain risk factors for delayed RTW would be stronger in the workplace MTBI group. In single predictor models, perceived injustice, post-concussion symptoms, and having uncertain (vs. optimistic) recovery expectations at the time of clinic intake were associated with lower likelihood of returning to work 4–5 months later (i.e., by 6–7 months following injury). After adjusting for all variables in the model, only greater post-concussion symptoms were associated with a lower likelihood of RTW at follow-up. This is consistent with previous studies[23,51] demonstrating that symptom burden is predictive of a protracted recovery. Additionally, the multivariate model showed an association between lower traumatic stress symptoms and worse outcome. We suspect that this finding is an artefact of statistical suppression. Traumatic stress was not associated with RTW in unadjusted (single predictor) modeling but was highly correlated with post-concussion symptoms (r = .60). The collinearity of these measures likely enhanced the relationship between post-concussion symptoms and the outcomes (i.e., OR 1.06 to 1.10) and suppressed the relationship between traumatic stress and the outcome (i.e., 1.01 to 0.93). Contrary to our hypotheses that certain risk factors would be differentially associated with the workplace MTBI group, there were no statistically significant interactions effects.
This study has several noteworthy limitations. First, the workplace and non-workplace MTBI groups were not matched on the timing of their first assessment. This was an observational study and our two groups had inherently different access to healthcare. We employed a covariate adjustment for time since injury wherever possible. Second, we had very limited information on the screened participants who did not enroll in this study, precluding the analyses necessary to understand the presence and impact of selection bias. Third, as with all longitudinal studies, there was attrition. The rates of attrition were similar in both MTBI groups, and those who were lost to follow-up had similar demographics compared to those who were retrained. It seems unlikely that attrition biased the group comparisons of primary interest, workplace vs. non-workplace. Fourth, injury characteristics were assessed via self-report during a structured interview. Medical records were not available to review. Although self-reported data has limitations[71,72], the primary hypotheses regarding differences between the workplace and non-workplace MTBI groups should not be affected by these limitations. Fifth, the workplace and non-workplace MTBI groups differed on their treatment utilization following clinic intake. This imbalance is inherent to the health care system. Sixth, it is possible that we did not find group differences in outcomes because of sample size and statistical power limitations. However, the small magnitudes of the observed effect sizes suggest that large differences between work and non-work MTBIs are unlikely. Lastly, the follow-up period was not long enough to observe the outcome of interest (RTW) in large proportion of the sample, which precluded more powerful (time-to-event) statistical modeling techniques.
In summary, despite systemic differences in compensation and health care access, the workplace and non-workplace MTBI groups were similar at clinic intake and indistinguishable at follow-up, 6–7 months following injury. The workplace MTBI group had different demographic profiles (i.e., more men, lower education levels, fewer managerial/professional jobs) and greater traumatic stress symptoms at the initial assessment, but these differences did not translate into measurable divergence on any outcome measure. There was no evidence for risk factors for delayed RTW being more or less relevant to patients with workplace MTBI vs non-workplace MTBI. At the follow-up assessment 6–7 months after MTBI, a minority of individuals had achieved their pre-injury work productivity levels, and the large majority continued to meet symptom criteria for at least mild ICD-10 postconcussional syndrome. It is important to interpret these findings in the context of our study design. Participants in both groups were clinic-referred, slow-to-recover, highly symptomatic, and had disproportionately high rates of risk factors for poor outcome. They were not representative of all people who sustain an MTBI. However, the present findings may generalize to other outpatient clinic settings where patients with MTBI are commonly seen. This study highlights the need for intervention research to improve functional outcomes in patients with MTBI who present for outpatient care. An improved characterization of risk factors could inform a personalized medicine approach, whereby injured workers are matched to therapies designed to target the source of their heightened risk.
The authors wish to thank research assistants Mary Ellen Johnson and Sabrina Khan as well as the clinical staff at our recruitment sites, including Kelsey Davies, Jennifer Loffree, Grace Boutilier, and Trish Mahoney (GF Strong Rehab Centre), Deanna Yells, Heather MacNeil, Ali Sacks, Rod Macdonald and Andrew Fairbairn (Fraser Health Concussion Clinic), Denise Silva and Lesley Norris (Back in Motion), and Karilyn Lao, Wayne Tang, and Cyrus Huang (LifeMark).
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text:
|High School:||Northern Guilford|
2013-14: Lettered as a first-year ... expected to compete for one of the team's top positions again as a sophomore.
2012-13: Played a total of 13 rounds, posting a 99.9 scoring average ... recorded a top finish of 14th, which she accomplished at the season-opening Hollins Invitational (97-100--197) ... also tied for 14th at the ODAC Championship, shooting a 104-97--201 ... shot a low round of 96 in finishing with a 199 to tie for 17th at the W&L Invitational.
High School: Earned four letters as a member of the golf team at Northern Guilford High School ... served as a team captain for her senior season ... helped lead the team to a pair of conference championships ... team placed seventh at the state championship as a junior and was state runner-up as a senior ... four-time academic all-conference honoree ... earned all-conference honors as a first-year and senior ... was the team MVP as a first-year.
Personal: Undeclared major.

domain: sports


San Diego, California USA
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text:
Sledding is remarkably fun in the winter, when you are careful. Cold, wet, and beautiful white snow, blankets small hills in my home of Indiana in the shivering December-February months. But venturesome cardboard sledding, in California at anytime of the year, is loads more fun in my experience. I was 9 years old, and my elder sister and I flew out to the west coast into a busy suburban city near the Golden Gate Bridge to see our relatives on our mother’s side. As you may know of, California is known for their golden rolling hills of tall tan grass and hovering-over-homes redwood trees.
This hints the reader of some connection between the owl and Ultima from their protective natures, especially for Antonio. Antonio rushes to Ultima and discovers her in her dying throes, she says, “When I was a child… I was taught my life's work by a wise old man, a good man. He gave me the owl and he said that the owl was my spirit” (260). This finally gives proof to the reader that Ultima’s spirit was the owl. From this we see how much Ultima was supporting and protecting
He begins to notice the several features of the llano that make up its extraordinary appearance such as the water which runs beyond it. For the first time Tony recognizes the llano as more than just as a piece of land as he says “…the beauty of the llano unfolded before my eyes and the gurgling waters of the river sang to the hum of the turning earth.” At this point in Tony’s life he is finally conceiving the vast llano at its full potential unlike before. Anaya places water imagery in this quote to describe the appearance of the llano. He uses the word “gurgling” most likely to show that the water in a way is “alive” and so is the llano in this case. The word “unfolded” is also placed in the quote to demonstrate that the llano’s beauty, which was once covered away from him, opening up and revealing itself.
The Beauty in Nature A while back, maybe a year or so, I got the opportunity to go to outdoor science camp with my classmates. During my stay, I got to soak in the aspects of nature, like the running creek and fresh fallen snow, which made me develop an appreciation for nature. In Pablo Neruda’s poem, “Ode to enchanted light,” the speaker describes the beauty in nature, life, and light. In “Sleeping in the Forest,” a poem by Mary Oliver, nature is thought of as a place that’s shrouded in a mystical beauty and contentment. In both poems, the poets both use figurative language to express their appreciation for nature.
Growing up I was consumed by an interesting assortment of music. Musical influences were thrown at me from all directions. Whether it was the rock and roll and punk rock music my dad listened to, or the country and pop music my mom listened to, my ears were always full of different arrays of music. Having my grandma blasting gospel music and my older sister jamming Disney, music became a very influential part of my childhood. As a young child, I found myself drawn to Disney music.
I have many memories but, the one that is the most vivid is when my family and I went on vacation and went ice skating. This was the first vacation we had ever gone on as a family. My Family and I vacationed in the most elegant of nature. I thought to myself “Tennessee is so peaceful and heartwarming”. I enjoyed the way the sun shined through the trees so softly, the slight howling of the wind while it blew through my hair, and the heavy scent of oak trees in the middle of fall had me mesmerized.
“ Never be afraid to fall apart because it is an opportunity to rebuild yourself the way you wish you had been all along.” - Rae Smith. This quote made by Rae Smith helps to describe Theodore Taylor character Phillip in the novel “The Cay”. In the Cay, Phillip’s character reveals that personal strength comes from overcoming fears and obstacles standing in the way. Phillip shows three characteristics that he use to get over challenges: innocence, Afraidness, and braveness. In the beginning of the novel Philip can best described as Innocent.
This brings Holden’s good qualities and compassion to the reader’s attention. Another example of Holden caring for children is in the middle of chapter 25 when he sees profanity on the wall, he describes, “I thought how Phoebe and all the other little kids would see it, and how they’d wonder what the hell it meant” (Salinger 200). This is another example of Holden wanting to help children and keep them protected. This shows that Holden has good intentions and deserves readers’ compassion. In summary, Holden’s wish to keep children safe shows his good character and worthiness of
This is shown by Lennie’s dreams in OMAM. It is also shown by the children’s commitment to music in War Dance, and Walter Mitty’s change of heart in SLOWM. In John Steinbeck 's OMAM, hope is very important to the characters, especially Lennie. Lennie’s hope and dream to have a farm with rabbits helps him to keep going and not cause trouble where he is. In the article The Will and Ways of Hope, Scott Barry writes “... people with hope have both the will and the pathways and strategies necessary to achieve their goals”(Barry, 2).
This is shown through how Atticus teaches his kids, leading them to be intelligent and compassionate. Scout shows this when talking to Atticus about Mr. Ewell's death: “‘Yes sir, I understand,’ I reassured him. ‘Mr. Tate was right.’ Atticus disengaged himself and looked at me. “What do you mean?’ ‘Well, it’d be sort of like shootin‘ a mockingbird, wouldn’t it?’” (Lee 370).
TKAM Essay A person or event can make an impact on a child or young adolescent in either a positive or a negative way and can teach them a lesson that they can carry with them throughout their lives. In the novel To Kill A Mockingbird by Harper Lee, Scout Finch is given exquisite examples and terrible examples in her modest town. Scout discovers courage and cowardness from the ones who are surrounding her while growing older in Maycomb. (77) Scout Finch receives courage from her peers in her surroundings, mainly from her father Atticus Finch who presents many acts of courage in and outside of the counsel. For example, when Atticus is surrounded by a crowd of riled men because Atticus is upholding Thomas Robinson’s case, Atticus states,
The diction choices progress into detail throughout the scene, allowing for strong examples of imagery with familiar scenes. “Autumn again, and Boo’s children need him,” (374). For the duration of the passage, Scout defines the children as “his.” By the end, “his” children have become “Boo’s” children. Lee intended the reader to capture Boo’s mindset through her chosen diction. He had become their neighbor, their friend.
It shows us that everyone has the potential to grow, if one has the tenacity. He uses the anecdote to show us that children learn what they see, and empathy is one of the things one learns in a young age. For Tupac to use this image in his album after he met Maya, I think he wanted to express what he learned that day. That’s why Andrade used that picture to show the audience. Andrade choose to show us the two important things he tells his students, the first one being that they are going to be in their lives forever and two is that they loved them.

domain: literature


15 Lillian Street
Commute to Downtown Woburn
Almost all errands require a car.
A few nearby public transportation options.
15 Lillian Street has a Walk Score of 18 out of 100. This location is a Car-Dependent neighborhood so almost all errands require a car.
15 Lillian Street is a one minute walk from the Lowell Line at the Mishawum stop.
This location is in the Mishawum neighborhood in Woburn. Nearby parks include Kennedy Jr. High Park, Higgins Park and Forest Park.
Explore how far you can travel by car, bus, bike and foot from 15 Lillian Street.
15 Lillian Street has some transit which means a few nearby public transportation options.
354 Burlington - Downtown Boston
134 North Woburn - Wellington


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Some homebrewers call themselves "hop-heads" because of their absolute love of hops' flavor, aroma, and bitterness. They search far and wide for the freshest hops and pour ounce after ounce into their beers, constantly pushing the IBU envelope, and dry-hopping every brew. I've even seen brewers who've used 6 or 8 different types of hops in one beer! It may sound kind of crazy, but if you love hops, why not? I can definitely identify with "hop-heads" - I love the aroma and flavor of hops, and I love the unique taste each hop variety can bring to homebrew - whether it's spicy, floral, or otherwise. But the bottom line is that personally, I am much more in love with the flavor of malted barley. Call me a "malt-head" or whatever other moniker you can invent - I simply love the smell, the sweetness, the flavor, and the hundreds of ways barley can be roasted, toasted, caramelized, and smoked. I've always wondered what it would take to malt my own barley and so for the last couple months I have been searching the internet for information on home malting. It seems that there are not a huge amount of people out there malting their own barley, but I was able to get some general information to get started.
The basic process of malting begins with moistening the barley seeds so they start to germinate (sprout). The germination process causes the starchy insides of the seed to be converted into sugar - the stuff beer's made out of! These sugars are designed to feed the baby plant until it can find its own nutrient source. After the seeds have sprouted to a certain point and contain as much sugar as possible, you dry them out to stop the process. Then you "kiln" them to complete the drying process and to give them some flavor, and you can even roast them for more color and flavor.
To make crystal malt, you have to mash the green (freshly sprouted) barley, and then cook it at a high temperature to crystalize the sugars. The fact that crystal malt has already been mashed is why extract and partial mash brewers are able to simply steep these grains for a short time in hot water instead of putting them through an entire mashing process - it's already been done!
So I decided that I wanted to keep the process as simple as possible and attempt to produce a basic pale malt, and a small amount of crystal malt to add body and flavor.
Next I searched for a good online source of barley seed. I have read that you can usually acquire barley seed at any local feed store for very cheap, but I didn't know of any feed stores in my town, and I was also concerned that a feed store would sell me a product more designed for animal use and not for malting. Finally I found http://www.amazon.com/Organic-Barley-25-Ornamental-Barleygrass/dp/B000E7MU2K. This company will ship you a 25 pound bag of premium unhulled barley seed that has been produced completely organically. The fact that it was organic just made me more excited, since I entered into this whole undertaking to eliminate factory processing and to produce the ultimate fresh malt in a hands-on way. So the absence of chemical pesticides on the grain just felt even more natural and earthy. The barley was pretty inexpensive, but due to the weight the shipping was almost as much as the actual grain, and it took about a week to arrive at my home in Florida. I devised the following plan: I would break the malting up into two larger batches (designed to make the usual 5 gallons), and one small batch (approximately 2 gallons). The first large batch would consist of a pale malt and a small amount of crystal malt to make a hoppy American Pale Ale, the second large batch would be more roasted with a larger amount of crystal malt to make a toasted lager, and the leftover small batch I would smoke in my buddy Erin's smoker! (yet another thing I've always wanted to try).
Next I got a cheap digital scale from Walmart. I've always wanted one for making precise hop measurements, and now I had the perfect excuse, as I would need to separate the grain into portions (it would be impossible to malt the whole 25 pounds at once). So I weighed out 10.5 pounds of grain and dumped it into my 4 gallon brewpot. The first thing you have to do is clean the grain by filling the pot with water and running your hands through the grain. All the dead seeds, pieces of plant material, and other useless crap floats to the top and can easily be skimmed off and removed. The water turned slightly brown so I poured it off, then filled the pot up with fresh water to begin the soaking process.
I have read many different opinions about soaking the grains, but most call for some time under water, then some time with the water removed, until the grain begins to sprout. I basically did what I could around my busy work schedule, but it turned out to be approximately 6-8 hours wet followed by 6-8 hours dry and so on. I stirred the grains whenever I could, and within about 24 to 48 hours they started to "chit", which is when the first tiny root begins to protrude from the seed.
At this point I gave the barley one final rinse and then dumped it into three trays. Actually, two trays and one large wooden salad bowl (I have to work with what I've got). I covered the grains with loose plastic wrap, so that dust and bacteria would hopefully not fall into the grain from gravity (you can also use tin-foil). Many people use large clay pots for this stage, since they are porous and can absorb any extra water, but, once again, I just used what I had available.
During this stage of germination it is very important to stir the grains at least a couple times a day, as they actually produce heat during the process. This warm, moist environment is a perfect place for mold to grow, and if that happens you pretty much have to throw everything out and start again. I read online that the inside of a germinating pile of barley can exceed 104 degrees! (About the temperature of a jacuzzi) All I can tell you is that when I stirred up my grains, I scooped barley into my hand and could definitely feel a noticeable warm sensation! So just stir them as often as you can, and if your grain starts to smell like it's rotting, it probably is.
The germination of barley should take anywhere from 3-5 days. If you work full-time, like me, starting the process early in the week (like Monday night) will allow you to perform all your drying and kilning on the weekend, when there's more free time. This is important, since I would not reccomend leaving the oven unattended while at work.
You want to dry the barley and stop it from growing when the leave shoot (acrospire) is 75% to 100% the length of the kernel. But when the acrospire begins to grow, at first it grows inside the kernel! So the only way to find out it's length is to break open a few seeds and gently pull out the shoot. Another good indication is if the roots have developed up to 2 or 3 times the length of the seed - at this point the acrospire is usually at least 75% in length. Also, I noticed that once in a while you will find a kernel that has burst open and the acrospire is actually shooting out the side. It is very rare, like some kind of mutant or something, but it does allow you to easily see the length of the acrospire without breaking open any seeds. You can eat a few seeds at this point - they should have a sweet flavor - this means the starches have begun their conversion and it's time to dry the barley and lock in all that flavor!
But first, I needed to make my crystal malt. Basically, this involves mashing a portion of the sprouted barley (green barley), and then cooking it at a high temperature to dry it and caramelize the sugars. First, I weighed out the amount of barley to be mashed. Remember that at this point the weight of your barley is 40- 50% water, so you need to use practically double the weight that you eventually want to end up with, because after drying you will lose all that water weight.
Next, I put the barley into 4 small ziplock bags, sucked out as much air as possible, and sealed. I then heated up a couple gallons of water to 160 degrees and filled up my small mash tun, placing the bags of barley inside. No matter how hard you try to suck out the air, they will still float, so you have to place some kind of weight on top. I used a small sauce pan with my heavy glass sugar-bowl top inside for weight. When I closed the top the cooler lid sort of pushed the pan down (my mash tuns are made out of round Rubbermaid coolers), so I think my grains were pretty much underwater. After the 160 degree water hits your cool mash tun, and then after adding the grain bags, you should end up with a temperature somewhere in the low 150's - perfect! Now simply wait 1-2 hours - the same as mashing grain to make wort, except the grains never actually touch the water, so the sugars stay locked inside.
After that I removed the bags and dumped out the grains into my small roasting tray. I set my oven at 400 degrees and placed the tray inside (about halfway up). It takes a couple of hours, and you will obviously need to stir the grains every so often so they receive even heat. After a while you may hear some crackling sounds - this is normal, don't freak out! The caramelization has begun! At this point, monitor the grain closely and cook it as long and dark as you desire. If you want to create a "Carapils" style dextrin malt, simply dry the grains but don't darken them (this will add body to beer but not as much color or flavor). I kept mine in the oven as long as possible, and at a certain point the edges of the grain looked like they were getting burnt, so I removed them.
After that, I set the temperature at the lowest possible - 170 on my oven, dumped the rest of my grain into the two large roasting pans, and put them inside. I propped open the door slightly with a large plastic spoon. This keeps the temperature down (170 is actually a bit warm) and allows the air to circulate, aiding in the drying process. Once again, you must stir the grains as much as possible so they dry evenly. This takes 24-48 hours. My oven automatically shut down after 12 hours, and I had to reset the temp to continue.
When the grain is almost completely dry (chew some - it should be crunchy on the outside and slightly softer inside) it is time to "kiln". I set my oven at 250 degrees and kilned for about 2 1/2 - 3 hours. Stir often and, by God, enjoy that amazing smell that will now saturate your entire house!
The next step is to take the grain out of the oven, let it cool, and then remove the dried roots, which will now have turned brown and curled up. I accomplished this by taking small portions of grain and putting them into a wire-mesh strainer, and then circulating them with my hand at moderate pressure. Most of the dried roots fall off and pass through the strainer and the grains remain.
According to some of the internet posts I have read, it is best to then let the grain sit for a week or two before using it to make beer. I don't know why you need to do this, but for me it just simply worked out best since I wasn't ready to brew when my grains had finished the malting process. I left the grains in one of the roasting trays and covered them with tin-foil. I poured my crystal malt on top and it was definitely cool to see the contrast in color between the crystal and the pale malts.
That's it!!! I will blog again on my first "home-malted" brew day!
P.S. - I tried to take more close up pics of the grain chits and roots, but they kept coming out blurry on my camera. However I did manage to find a website with really nice germination close-ups posted by a dude who malts his own barley in the Phillipines. Check it out at http://beerme.wordpress.com/2007/04/09/challenge-ii-complete-malted-barley-at-home/
7/29/09: Just wanted to post a quick note after this entry. In case you haven't had a chance to read about all my brewing experiments with homemade malt, I was unable to get my efficiency higher than 35% (!!!) using the "home-malted" barley (very, very poor). I even tried germinating my seeds longer until the acrospires had all achieved about 100% seed length, and also milling the grains very thoroughly, but still had very low efficiency. I believe the only factor left to change would be the temperature at which I dried the grains. My oven will not go below 170 degrees F, and all the literature I read online called for around 50 degrees C (or 122 F). As you can see above, I left the door of the oven open and tried to circulate the grains often, but I am thinking that maybe the heat was still too much and managed to damage the potential of the barley in some way. To remedy this, the only option that I personally would have available would be to dry the grain in the sun. I live in south Florida, so this would work great in the wintertime, but right now in the summer it rains every day and I wouldn't be able to leave anything outside for long. So I can try the sun-drying technique this winter and report back afterwards. Also, I could try a different barley source. I thought I had the coolest barley around, but who knows? I would be interested to see if a different source may be better suited for malting. Good luck to anyone out there trying this, please post a comment if you have anything to add. Thanks.
8/16/09 - I almost forgot, here is another method I tried using a rigged up fan to circulate the heat and try not to damage the grains. The fan blows air into the oven, and then it escapes through the side of a towel I put over the opening. Results were questionable:

domain: agronomy