Exploring Work–Life Issues in Provincial Corrections Settings
Bibliographic record
Abstract
Correctional nurses hold a unique position within the nursing profession as their work environment combines the demands of two systems, corrections and health care. Nurses working within these settings must be constantly aware of security issues while ensuring that quality care is provided. The primary role of nurses in correctional health care underscores the importance of understanding nurses' perceptions about their work. The purpose of this study was to examine the work environment of nurses working in provincial correctional facilities. A mixed-methods design was used. Interviews were conducted with 13 nurses and healthcare managers (HCMs) from five facilities. Surveys were distributed to 511 nurses and HCMs in all provincial facilities across the province of Ontario, Canada. The final sample consisted of 270 nurses and 27 HCMs with completed surveys. Participants identified several key issues in their work environments, including inadequate staffing and heavy workloads, limited control over practice and scope of practice, limited resources, and challenging workplace relationships. Work environment interventions are needed to address these issues and subsequently improve the recruitment and retention of correctional nurses.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".