The Role of Nursing Leadership in Ensuring a Healthy Workforce in Corrections
Bibliographic record
Abstract
In Canada, responsibility for corrections is divided between federal, provincial and territorial governments, with nurses being the largest group of healthcare professionals working in correctional institutions (penitentiaries, jails, prisons, correctional centres and secure correctional treatment centres) across the country. Correctional institutions are among the most challenging workplace settings for nurses, as they face competing tensions between the provision of quality care and strict security requirements for safety. They also experience unique workforce issues with high reports of burnout and emotional exhaustion. Nursing leadership at all levels of the correctional system is critical in creating work environments that optimize workplace well-being and minimize burnout. The purpose of this paper is to discuss the role of nursing leadership in facilitating and enabling a healthy workforce in corrections. Minimal research has examined leadership and healthy work environments in correctional institutions. Several authors have, however, discussed transformational leadership as a strategy to positively influence correctional nursing practice. In this article, we expand on this previous work to describe the full range leadership model and how it can be used to form the foundation of effective leadership and support the creation of healthy work environments in the correctional context.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".