The impact of funding models on the integration of registered nurses in primary health care teams: protocol for a multi-phase mixed-methods study in Canada
Bibliographic record
Abstract
BACKGROUND: Family practice registered nurses co-managing patient care as healthcare professionals in interdisciplinary primary care teams have been shown to improve access, continuity of care, patient satisfaction, and clinical outcomes for patients with chronic diseases while being cost-effective. Currently, however, it is unclear how different funding models support or hinder the integration of family practice nurses into existing primary health care systems and interdisciplinary practices. This has resulted in the underutilisation of family practice nurses in contributing to high-quality patient care. METHODS: This mixed-methods project is comprised of three studies: (1) a funding model analysis; (2) case studies; and (3) an online survey with family practice nurses. The funding model analysis will employ policy scans to identify, describe, and compare the various funding models used in Canada to integrate family practice nurses in primary care. Case studies involving qualitative interviews with clinic teams (family practice nurses, physicians, and administrators) and family practice nurse activity logs will explore the variation of nursing professional practice, training, skill set, and team functioning in British Columbia, Nova Scotia, Ontario, and Quebec. Interview transcripts will be analysed thematically and comparisons will be made across funding models. Activity log responses will be analysed to represent nurses' time spent on independent, dependent, interdependent, or non-nursing work in each funding model. Finally, a cross-sectional online survey of family practice nurses in Canada will examine the relationships between funding models, nursing professional practice, training, skill set, team functioning, and patient care co-management in primary care. We will employ bivariate tests and multivariable regression to examine these relationships in the survey results. DISCUSSION: This project aims to address a gap in the literature on funding models for family practice nurses. In particular, findings will support provincial and territorial governments in structuring funding models that optimise the roles of family practice nurses while establishing evidence about the benefits of interdisciplinary team-based care. Overall, the findings may contribute to the integration and optimisation of family practice nursing within primary health care, to the benefit of patients, primary healthcare providers, and health care systems nationally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".