Recruit firefighters: a longitudinal investigation of mental health and work
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the contribution of work to self-reported mental health symptoms in fire service members. Design/methodology/approach In 2004, the first wave of this data collection was completed with all members of a fire department in a small northern center in British Columbia. The members completed a series of questionnaires measuring mental health, personality and satisfaction. Since 2004, all recruit members entering the department have also completed the same set of questionnaires shortly after hiring. Subsequently, in 2016–2017, the full sample, including recruit members, were invited to complete the Wave 2 data collection cycle, which included a set of questionnaires very similar to that collected in Wave 1. Findings The recruit sample reported significantly fewer mental health symptoms, as compared to career firefighters, at Time 1 (prior to workplace exposure). However, at Time 2 (after workplace exposure), no difference between the groups was evident. Research limitations/implications It is possible that recruit firefighters reported more positive mental health because of social desirability bias upon beginning a new job. Practical implications These results suggest that service as a firefighter could potentially have an impact on mental health and efforts should be made to mitigate this impact. Originality/value To the authors’ knowledge, the current research is the first study that has followed recruit firefighters longitudinally in an effort to prospectively evaluate the impact of workplace exposure on mental health.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".