Theory-Informed Strategies to Guide Policy, Practice, Education and Research About Registered Nurses in Primary Care
Bibliographic record
Abstract
Many primary care leaders remain unclear about how to embed registered nurses (RNs) into primary care practices. This paper identifies theoretical groundwork and measurement strategies to expand primary care RN roles. We facilitated deliberative dialogue, including breakout sessions, with a target audience of 68 participants from primary care research, policy and clinical organizations. Discussion was recorded and analyzed until themes emerged. Results illuminated challenges with inconsistent titles, lack of competencies and difficulties measuring RN contributions. Theoretical frameworks (e.g., Donabedian's model and the co-management model) and effective measurement strategies may best inform practice, policy and research to enhance RN roles 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 | 0.201 | 0.170 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".