Prevalence of Psychiatric Disorders Among Toronto Hospital Workers One to Two Years After the SARS Outbreak
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine the incidence of psychiatric disorders among health care workers in Toronto in the one- to two-year period after the 2003 outbreak of severe acute respiratory syndrome (SARS) and to test predicted risk factors. METHODS: New-onset episodes of psychiatric disorders were assessed among 139 health care workers by using the Structured Clinical Interview for DSM-IV and the Clinician-Administered PTSD Scale. Past history of psychiatric illness, years of health care experience, and the perception of adequate training and support were tested as predictors of the incidence of new-onset episodes of psychiatric disorders after the SARS outbreak. RESULTS: The lifetime prevalence of any depressive, anxiety, or substance use diagnosis was 30%. Only one health care worker who identified the SARS experience as a traumatic event was diagnosed as having PTSD. New episodes of psychiatric disorders occurred among seven health care workers (5%). New episodes of psychiatric disorders were directly associated with a history of having a psychiatric disorder before the SARS outbreak (p=.02) and inversely associated with years of health care experience (p=.03) and the perceived adequacy of training and support (p=.03). CONCLUSIONS: Incidence of new episodes of psychiatric disorders after the SARS outbreak were similar to or lower than community incidence rates, which may indicate the resilience of health care workers who continued to work in hospitals one to two years after the SARS outbreak. In preparation for future events, such as pandemic influenza, training and support may bolster the resilience of health care workers who are at higher risk by virtue of their psychiatric history and fewer years of health care experience.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".