Utilization of Nurse Practitioners to Increase Patient Access to Primary Healthcare in Canada – Thinking Outside the Box
Bibliographic record
Abstract
In the past decade, all Canadian provinces and territories have launched various team-based primary healthcare initiatives designed to improve access and continuity of care. Nurse practitioners (NPs) are increasingly becoming integral members of primary healthcare teams across the country. This paper draws on the results of a scoping review of the literature and qualitative key informant interviews conducted for a decision support synthesis about advanced practice nursing in Canada. We describe and analyze two novel approaches to NP integration designed to address the gap in patient access to primary healthcare: (1) the integration of NPs in traditional fee-for-service practices in British Columbia, and (2) the creation of NP-led clinics in Ontario. Although fee-for-service remuneration has been a barrier to collaborative practice, the integration of government-salaried NPs into fee-for-service practices in British Columbia has enabled the creation of inter-professional teams, and based on early evaluation findings, has increased patient access to care and patient and provider satisfaction. NP-led clinics are designed to provide inter-professional care in communities with high numbers of patients who do not have a regular primary healthcare provider. Given the shortage of physicians in communities where these clinics are being introduced, the ratio of physicians to NPs is lower than in other primary healthcare delivery models, and physicians function in more of a consulting role. Initial evaluation of the first of 26 NP-led clinics indicates increased access to care and high levels of patient and provider satisfaction. Implementing a creative mosaic of collaborative primary healthcare models that are responsive to patient needs challenges traditional assumptions about professional roles and responsibilities. To address this challenge, we endorse a recommendation that governments establish a mechanism to bring together both physician and non-physician primary healthcare providers to advise on primary healthcare policy development and implementation.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".