Occupational Stress, Correctional Officers, and Training for the Job: Probing Sources of Stress During the Correctional Service of Canada’s Correctional Training Program
Bibliographic record
Abstract
Occupational stress remains a remarkable problem among correctional officers. While the scholarship on correctional services has scrutinized correctional work to identify and analyze sources and consequences of stress, correctional training has received little attention. Drawing on the literature on sources of stress in corrections work, we analyze and compare whether sources of stress on the job overlap with those of correctional training. We base our analysis on interviews with correctional officers from Canada’s federal prison system who were interviewed while completing the Correctional Service of Canada’s Correctional Training Program. Findings suggest that sources of stress in training are not consistent with those of correctional work. The training program conditions succeed in preparing recruits to manage pressure, strain, and anxiety. However, the program does not necessarily equip recruits to deal with on-the-job stress, and does little to eliminate the occupational stressors and mental health disorders that too often emerge during occupational tenure. Correctional training programs in Canada and beyond must ensure that recruits are equipped with tools to deal with the specific sources of stress and possibly eliminate such sources in the course of work.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".