Canadian Correctional Officers’ Experiences of Workplace Safety and Stress During the COVID-19 Pandemic
Bibliographic record
Abstract
This research examined the experiences of Canadian correctional officers (COs) in providing essential services during the COVID-19 pandemic. The study applied theoretical models of stress to explore correlates of COVID-related stress in the correctional setting. Provincial COs (N = 596) took part in an online survey at the end of 2020. Only a quarter of participants reported they felt safe at their workplace during the pandemic. Participants also reported high levels of work stress typified by significant changes to their job responsibilities and role confusion stemming from pandemic-related policies. While most participants agreed that their workplace successfully implemented safeguards, they indicated key downstream considerations were lacking and perceived low levels of certain types of support. Several individuallevel, situational workplace stressors, and coping resources were revealed to be significant correlates of COVID-19 stress. In estimating a hierarchical linear regression model, COVID-19 stress was found to be primarily driven by perceived safety precaution implementation when controlling for individual-level vulnerabilities. Findings examine the impact of COVID-19 among frontline workers in forgotten sectors such as corrections to contribute knowledge that can be used to support COs’ well-being in the face of future infectious disease planning, as well as implications for policy planning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".