Integrating nurse practitioners into primary care: policy considerations from a Canadian province
Bibliographic record
Abstract
BACKGROUND: The integration of nurse practitioners (NPs) into primary care health teams has been an object of interest for policy makers seeking to achieve the goals of improving care, increasing access, and lowering cost. The province of Alberta in Canada recently introduced a policy aimed at integrating NPs into existing primary care delivery structures. This qualitative research sought to understand how that policy - the NP Support Program (NPSP) - was viewed by key stakeholders and to draw out policy lessons. METHODS: Fifteen semi-structured interviews with NPs and other stakeholders in Alberta's primary care system were conducted, recorded, transcribed and analyzed using the interpretive description method. RESULTS: Stakeholders predominantly felt the NPSP would not change the status quo of limited practice opportunities and the resulting underutilization of primary care NPs in the province. Participants attributed low levels of NP integration into the primary care system to: 1) financial viability issues that directly impacted NPs, physicians, and primary care networks (PCNs); 2) policy issues related to the NPSP's reliance on PCNs as employers, and a requirement that NPs panel patients; and 3) governance issues in which NPs are not afforded sufficient authority over their role or how the key concept of 'care team' is defined and operationalized. CONCLUSIONS: In general, stakeholders did not see the NPSP as a long-term solution for increasing NP integration into the province's primary care system. Policy adjustments that enable NPs to access funding not only from within but also outside PCNs, and modifications to allow greater NP input into how their role is utilized would likely improve the NPSP's ability to reach its goals.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.033 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".