A process-based framework to guide nurse practitioners integration into primary healthcare teams: results from a logic analysis
Bibliographic record
Abstract
BACKGROUND: Integrating Nurse Practitioners into primary care teams is a process that involves significant challenges. To be successful, nurse practitioner integration into primary care teams requires, among other things, a redefinition of professional boundaries, in particular those of medicine and nursing, a coherent model of inter- and intra- professional collaboration, and team-based work processes that make the best use of the subsidiarity principle. There have been numerous studies on nurse practitioner integration, and the literature provides a comprehensive list of barriers to, and facilitators of, integration. However, this literature is much less prolific in discussing the operational level implications of those barriers and facilitators and in offering practical recommendations. METHODS: In the context of a large-scale research project on the introduction of nurse practitioners in Quebec (Canada) we relied on a logic-analysis approach based, on the one hand on a realist review of the literature and, on the other hand, on qualitative case-studies in 6 primary healthcare teams in rural and urban area of Quebec. RESULTS: Five core themes that need to be taken into account when integrating nurse practitioners into primary care teams were identified. Those themes are: planning, role definition, practice model, collaboration, and team support. The present paper has two objectives: to present the methods used to develop the themes, and to discuss an integrative model of nurse practitioner integration support centered around these themes. CONCLUSION: It concludes with a discussion of how this framework contributes to existing knowledge and some ideas for future avenues of study.
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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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".