“If You’re Being Forced, You’re Being Forced”: A Qualitative Examination of How Overtime Affects Correctional Officers
Bibliographic record
Abstract
Canadian correctional institutions operate with insufficient staffing levels, leading to occupational stress that negatively affects prisons’ overall functioning as well as staff and incarcerated people’s wellness. The literature suggests a direct correlation between staffing levels and correctional officer (CO) wellness, but little is known about the nuances of the effects of forced overtime on COs’ well-being. Drawing on the job demand–control–support (JDCS) model, our qualitative study examines, through semi-structured interviews with federally employed COs in Canada ( n = 93), how overtime affects the health and wellness of COs in concrete and discursive ways. Findings focus on the relationship between staff shortages, overtime, mental health, and a perceived lack of agency in accordance with the JDCS model. Practical implications and potential strategies to mediate the effects of forced overtime in correctional spaces are discussed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".