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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".