Correctional nursing in Canada’s Prairie provinces: Roles, responsibilities, and learning needs
Bibliographic record
Abstract
BACKGROUND: Nurses represent the largest group of health care professionals working with incarcerated persons, yet there is limited understanding of their learning needs, or their roles and responsibilities; and what is known is poorly disseminated. PURPOSE: The goal of this research was to describe the roles, responsibilities, and learning needs of correctional nurses practicing in provincial correctional facilities in Alberta and Manitoba, and to add these data to the existing data set from Saskatchewan. METHODS: Three hundred and forty nurses working in provincial correctional facilities in western Canada were invited to complete a self-administered online survey consisting of a Learning Needs Assessment questionnaire (demographic information, knowledge and learning needs, and professional development); and the Staff Questionnaire (which targeted specific skill sets relevant to clinical practice in secure environments). Eighty-two nurses completed the online survey (overall response rate 24%). RESULTS: Overall, those who participated were experienced in nursing and correctional nursing. The learning needs they identified aligned with their correctional nursing roles and unique practice settings. In particular, issues related to the care of incarcerated persons with mental health disorders and related care were paramount (self-harming behaviours, suicide, mental health assessments in general). In response to the five comprehensive skill sets assessed in the Staff Questionnaire, respondents rated their involvement and importance of the individual skills as important to varying degrees. CONCLUSIONS: The results of this survey shed light on contemporary developments in correctional nursing within provincial correctional facilities in western Canada and provide a foundation for continuing professional education and development, practice, and future research initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".