Canadian Provincial and Territorial Correctional Worker Mental Health and Well-Being Study (CWMH): Navigating Practical and Unanticipated Methodological Challenges
Bibliographic record
Abstract
Previous research assessing correctional worker (CW) mental health has seldom assessed for differences based on jurisdiction or diverse occupational categories. The current study was designed to provide a nuanced quantitative examination of mental health disorder prevalence and related problems among CWs and to qualitatively explore the varying social contexts surrounding CW well-being. We reflect on how we overcame unanticipated challenges and disruptions (e.g., technology, COVID-19 pandemic) throughout the design, launch, and analysis of the survey, and illustrate how our national study, driven by a rigorous methodological approach and collaborative research design, builds on the extant CW mental health and wellness literature.
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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 | 0.067 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".