Unseen, unheard, undervalued: advancing research on registered nurses in primary care
Bibliographic record
Abstract
Julia Lukewich, Marie-Eve Poitras and Maria Mathews describe the current state of family practice nursing in Canada and explore the reasons for the lack of research on this topic Funding model reforms have led to an increase in the number of nurses in primary care in Canada. Family practice nurses work alongside physicians and other healthcare providers, and are key members of primary care teams. Despite this, there remains a lack of clarity regarding the contributions of this unique role, as well as the absence of coordinated leadership and efforts to advance knowledge in this area. We describe the current state of family practice nursing in Canada and discuss challenges to generating evidence on roles, activities, and outcomes. We also provide recommendations to facilitate the advancement of nursing research that addresses primary care provision. Challenges include the absence of standardised terms for this role, a lack of distinction surrounding different regulated nursing designations in primary care, and the need for greater visibility. High-quality research will strengthen the evidentiary base from which to educate providers, inform administrators/policy-makers, and improve primary care outcomes.
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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 | 0.141 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".