Attending to power differentials: How <scp>NP</scp>‐led group medical visits can influence the management of chronic conditions
Bibliographic record
Abstract
OBJECTIVE: In Canada, primary care reform has encouraged innovations, including nurse practitioners (NPs) and group medical visits (GMVs). NP-led GMVs provide an opportunity to examine barriers and enablers to implementing this innovation in primary care. DESIGN: An instrumental case study design (n=3): two cases where NPs were using GMVs and one case where NPs were not using GMVs, was completed. In-depth interviews with patients and providers (N=24) and 10 hours of direct observation were completed. Interpretive descriptive methods were used to analyse data. RESULTS/FINDINGS: Two main themes were identified: (i) acquisition of knowledge and (ii) GMVs help shift relationships between patients and health-care providers. Participants discussed how patients and providers learn from one another to facilitate self-management of chronic conditions. They also discussed how the GMV shifts inherent power differentials between providers and between patients and providers. DISCUSSION: NP-led GMVs are a method of care delivery that harness NPs' professional agency through increased leadership and interprofessional collaboration. GMVs also facilitate an environment that is patient-centred and interprofessional, providing patients with increased confidence to manage their chronic conditions. The GMV provides the opportunity to meet both team-based and patient-centred health-care objectives and may disrupt inherent power differentials that exist in primary 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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".