Suffering in Silence: Work and Mental Health Experiences among Provincial Correctional Workers in Canada
Bibliographic record
Abstract
In the course of their duties, correctional employees face exposure to a variety of potentially psychologically traumatic events (PPTEs). Recent research points to an array of consequences of work experiences on the psychological well-being of correctional staff, including the development of mental health disorders such as posttraumatic stress disorder, general anxiety disorder, and major depressive disorder. Drawing on an open-ended survey response among provincial and territorial correctional employees (n = 269) in Canada, we consider the experiences of correctional employees who self-report an anxiety, mood, or other mental health disorder, with a particular focus on how such experiences are tied to work conditions and occupational environments. Findings demonstrate that, for many, mental health struggles are intimately tied to both operational and organizational factors – the former referring to job duties and the latter referring to social relations of work. How mental health status is navigated is intimately shaped by occupational norms and meanings tied to mental health, namely stigma. Despite the perceived link between work and mental health outcomes, mental health suffering is understood and responded to as a private problem – with fallout on the personal lives and welfare of staff. We discuss the implications of training paradigms and general understandings of mental health responsibility.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".