Pervasive Uncertainty under Threat: Mental Health Disorders and Experiences of Uncertainty for Correctional Workers
Bibliographic record
Abstract
Exposure to potentially psychologically traumatic events for correctional workers is high. However, the mechanisms driving the high prevalence are relatively unexplained. Using data from a cross-sectional, online survey of correctional service workers ( n = 845) in Ontario, Canada, collected in 2017–2018, we assess the prevalence of mental disorders with a specific focus on uncertainty in the workplace and between correctional roles. We find that correctional officers, institutional governance, and probation/parole officers appear most at risk of mental disorders (prevalence of any mental disorder was 56.9%, 60.3%, and 59.2%, respectively). We argue slightly lower prevalence among institutional wellness, training, and administrative staff may result in part from their more predictable work environment, where they have more control. The results reaffirm a need for evidence-based proactive mental health activities, knowledge translation, and treatment and a need to explore how authority without control (i.e., unpredictability at work) can inform employee 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".