The Acceptability and Feasibility of Implementing an Online Educational Intervention With Nurses in a Provincial Prison Context
Bibliographic record
Abstract
BACKGROUND: Correctional nursing requires a strong knowledge base with access to continuing education (CE) to maintain and enhance competencies. Nurses working in provincial prisons have reported many challenges in accessing CE, with online learning being identified as a potential solution. Limited research was found, however, which examined the correctional context in the development and delivery of online learning for nurses. The purpose of this study was to develop an online educational intervention tailored to correctional nurses and determine the feasibility and acceptability of implementing the intervention in a provincial prison context. METHODS: A sequential mixed methods study was conducted. Participants included nurses from three correctional settings in the province of Ontario, Canada. Semistructured interviews examined contextual factors and educational needs. Delphi surveys determined the educational topic. Preintervention and postintervention questionnaires examined the context, educational content, and intervention's acceptability and feasibility. RESULTS: The online intervention focused on mental health and addictions with two 30-minute webinars delivered back-to-back over 15 weeks. Respondents expressed satisfaction with the convenience of online learning at work using short webinars, as well as the topics, relevance of information, and teaching materials, but dissatisfaction with presentation style. The feasibility of the intervention was limited by access to technology, time to attend, education space, and comfort with technology. DISCUSSION: The findings from this study provide insight to guide the future development of online CE for correctional nurses. If changes are made within correctional facilities in collaboration with nurses and managers, online learning holds the potential to facilitate access to ongoing professional development.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".