Optimizing the Role of Registered Practical Nurses in the Operating Room: A Two-Phase Qualitative Descriptive Study
Bibliographic record
Abstract
BackgroundCurrent nursing shortages are shifting approaches to health human resource planning. Broad changes are being implemented to support system planning, however, there is a need to engage in targeted strategies that address shortages in specialty nursing areas, such as the operating room.PurposeThe purpose of this study was to explore how Registered Practical Nurses (RPNs) are currently utilized within operating room settings in Ontario, Canada.MethodsA two-phase qualitative descriptive study design was conducted. Phase 1 consisted of an online survey and Phase 2 consisted of individual, semi-structured virtual interviews. Participants included nurses working in urban and community hospitals and/or private clinics. Descriptive statistics were used to report participant demographic data, and qualitative data were analyzed using inductive content analysis.ResultsSixty-five participants completed the survey, and 13 participants completed the semi-structured interviews. Participants identified differences in RPN role utilization within different healthcare settings, teamwork and work culture. Recommendations for RPN leadership opportunities, policy support, professional development, and the role of professional nursing organizations were also identified.ConclusionGiven the complex nature of healthcare systems, new models of care, and evolving scopes of practice for healthcare providers, it is important to consider how RPNs can be further utilized to support patient care including specialty areas. Re-evaluating the roles and responsibilities of RPNs in healthcare is essential to strengthen the nursing workforce and prepare for ongoing human resource challenges.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".