Correctional nursing: a study protocol to develop an educational intervention to optimize nursing practice in a unique context
Bibliographic record
Abstract
BACKGROUND: Nurses are the primary healthcare providers in correctional facilities. A solid knowledge and expertise that includes the use of research evidence in clinical decision making is needed to optimize nursing practice and promote positive health outcomes within these settings. The institutional emphasis on custodial care within a heavily secured, regulated, and punitive environment presents unique contextual challenges for nursing practice. Subsequently, correctional nurses are not always able to obtain training or ongoing education that is required for broad scopes of practice. The purpose of the proposed study is to develop an educational intervention for correctional nurses to support the provision of evidence-informed care. METHODS: A two-phase mixed methods research design will be used. The setting will be three provincial correctional facilities. Phase one will focus on identifying nurses' scope of practice and practice needs, describing work environment characteristics that support evidence-informed practice and developing the intervention. Semi-structured interviews will be completed with nurses and nurse managers. To facilitate priorities for the intervention, a Delphi process will be used to rank the learning needs identified by participants. Based on findings, an online intervention will be developed. Phase two will involve evaluating the acceptability and feasibility of the intervention to inform a future experimental design. DISCUSSION: The context of provincial correctional facilities presents unique challenges for nurses' provision of care. This study will generate information to address practice and learning needs specific to correctional nurses. Interventions tailored to barriers and supports within specific contexts are important to enable nurses to provide evidence-informed care.
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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 | 0.040 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".