Educational role of nurse practitioners in a family practice centre
Bibliographic record
Abstract
Objective To examine the role of nurse practitioners (NPs) as educators of family medicine residents in order to better understand the interprofessional educational dynamics in a clinical teaching setting. Design A qualitative descriptive approach, using purposive sampling. Setting A family practice centre that is associated with an academic department of family medicine and is based in an urban area in southern Ontario. Participants First-year (8 of 9) and second-year (9 of 10) family medicine residents whose training program was based at the family practice centre, and all NPs (4 of 4) who worked at the centre. Methods Semistructured interviews were conducted, which were audiotaped and transcribed. An iterative approach was used for coding and analysis. Data management software guided organization and analysis of the data. Main findings Four interconnected themes were identified: role clarification, professional identity formation, factors that enhance the educational role of NPs, and factors that limit the educational role of NPs. Although residents recognized NPs’ value in team functioning and areas of specialized knowledge, they were unclear about NPs’ scope of practice. Depending on residents’ level of training, residents tended to respond differently to teaching by NPs. More of the senior residents believed they needed to think like physicians and preferred clinical teaching from physician teachers. Junior residents valued the step-by-step instructional approach used by NPs, and they had a decreased sense of vulnerability when being taught by NPs. Training in teaching skills was helpful for NPs. Barriers to providing optimal education included opportunity, time, and physician attitudes. Conclusion The lack of an intentional orientation of family medicine residents to NPs’ scope of practice and educational role can lead to difficulties in interprofessional education. More explicit recognition of the evolving professional identity of family medicine residents might decrease resistance to teaching by NPs and ensure that interprofessional teaching and learning strategies are effective. Faculty development opportunities for all educators are required to manage these issues, both to ensure teaching competencies and to reinforce positive interprofessional collaboration.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".