The Effect of Employment Status in Postdisaster Recovery: A Longitudinal Comparative Study Among Employed and Unemployed Affected Residents
Bibliographic record
Abstract
Population studies have shown that employed adults are healthier than unemployed adults. In this study, we examined whether this "healthy worker effect" is relevant in postdisaster mental health by examining whether trauma-exposed employed individuals have lower postdisaster initial mental health problems and/or whether they recover faster than trauma-exposed unemployed individuals. We compared the course of postevent intrusion and avoidance reactions, anxiety, depression, and sleeping difficulties of employed residents (n = 291) and unemployed residents (n = 269) affected by a fireworks disaster in a residential area of Enschede, The Netherlands. Measurements took place at 2-3 weeks (T1), 18 months (T2), and 4 years (T3) postdisaster. We used linear mixed-effect models to examine the course of mental health problems. Employment status was relevant, to a degree, in posttrauma recovery; although affected employed residents had significantly lower levels of mental health problems (initially and over time) than the unemployed, ds = 0.41-0.72, the recovery rate was the same for both groups. At T1 (neglecting the DSM 1-month criterion), T2, and T3, the prevalence of probable posttraumatic stress disorder was 45.4%, 18.9%, and 11%, respectively, among employed individuals, and 70.1%, 32.5%, and 30% among unemployed individuals. We concluded that research into the mental health of disaster victims should take employment status into account. Regarding postdisaster care, unemployed individuals may need special attention; although they may recover at the same rate as employed individuals, they suffer from more severe mental health problems, even years after the disaster.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".