CCWORK protocol: a longitudinal study of Canadian Correctional Workers’ Well-being, Organizations, Roles and Knowledge
Bibliographic record
Abstract
INTRODUCTION: Knowledge about the factors that contribute to the correctional officer's (CO) mental health and well-being, or best practices for improving the mental health and well-being of COs, have been hampered by the dearth of rigorous longitudinal studies. In the current protocol, we share the approach used in the Canadian Correctional Workers' Well-being, Organizations, Roles and Knowledge study (CCWORK), designed to investigate several determinants of health and well-being among COs working in Canada's federal prison system. METHODS AND ANALYSIS: CCWORK is a multiyear longitudinal cohort design (2018-2023, with a 5-year renewal) to study 500 COs working in 43 Canadian federal prisons. We use quantitative and qualitative data collection instruments (ie, surveys, interviews and clinical assessments) to assess participants' mental health, correctional work experiences, correctional training experiences, views and perceptions of prison and prisoners, and career aspirations. Our baseline instruments comprise two surveys, one interview and a clinical assessment, which we administer when participants are still recruits in training. Our follow-up instruments refer to a survey, an interview and a clinical assessment, which are conducted yearly when participants have become COs, that is, in annual 'waves'. ETHICS AND DISSEMINATION: (File No. 20190481). Participation is voluntary, and we will keep all responses confidential. We will disseminate our research findings through presentations, meetings and publications (e.g., journal articles and reports). Among CCWORK's expected scientific contributions, we highlight a detailed view of the operational, organizational and environmental stressors impacting CO mental health and well-being, and recommendations to prison administrators for improving CO well-being.
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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 | 0.065 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.088 | 0.015 |
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".