Prevalence of Mental Ill-Health in a Cohort of First Responders Attending the Fort McMurray Fire
Bibliographic record
Abstract
Objectives: The study was designed to estimate the prevalence of mental disorders in a cohort of firefighters who had been deployed to a devastating fire in Fort McMurray, Alberta, in 2016. Methods: A cohort of firefighters was established and followed up by online questionnaires. The contact in October 2018 to March 2019 included the PCL-5 questionnaire screening for post-traumatic stress disorder (PTSD) and the Hospital Anxiety and Depression Scale (HADS) screening for anxiety and depression. A sample was selected comprising all scoring ≥31 on the PCL-5 or ≥12 on either scale of the HADS, 30% of those scoring 8 to 11 on the HADS, and 10% of those with lower scores on all scales. This sample was assessed through a structured clinical interview to categorize disorders as defined in Diagnostic and Statistical Manual for Mental Disorders, fifth edition ( DSM-5). Interviews were carried out face-to-face or by telephone between August 2019 and February 2020. Diagnoses in the interview sample were reweighted to obtain prevalence estimates for the whole cohort. In an analysis of receiver operating characteristics (ROC), possible cut points for scores from each screening questionnaire were examined. Results: In 2018 to 2019, 1,000 of the cohort of 1,234 firefighters completed the HADS and 998 completed the PCL-5. Of these, 282 were identified for structured clinical interviews for DSM-5 (SCID) assessment. Interviews were carried out with 192. Among those assessed, 40.6% met the criteria for PTSD, 30.7% for an anxiety disorder, and 28.5% for a depressive disorder. When reweighted to allow for sampling and losses to assessment, cohort prevalence estimates were as follows: PTSD 21.4% (15.7% to 29.1%), anxiety disorders 15.8% (11.0% to 22.5%), and depressive disorders 14.3% (9.9% to 20.8%). Lower prevalence estimates were obtained when using the cut point with least misclassification in the ROC analysis. Conclusion: Using the gold-standard SCID assessment, high rates of mental disorders were found in this cohort of firefighters who had experienced a devastating fire. Fewer cases would have been identified by screening questionnaire alone.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".