Pre–post analysis of the impact of British Columbia nurse practitioner primary care clinics on patient health and care experience
Bibliographic record
Abstract
OBJECTIVE: This study aims to evaluate the impact of a primary care nurse practitioner (NP)-led clinic model piloted in British Columbia (Canada) on patients' health and care experience. DESIGN: The study relies on a quasi-experimental longitudinal design based on a pre-and-post survey of patients receiving care in NP-led clinics. The prerostering survey (T0) was focused on patients' health status and care experiences preceding being rostered to the NP clinic. One year later, patients were asked to complete a similar survey (T1) focused on the care experiences with the NP clinic. SETTING: To solve recurring problems related to poor primary care accessibility, British Columbia opened four pilot NP-led clinics in 2020. Each clinic has the equivalent of approximately six full-time NPs, four other clinicians plus support staff. Clinics are located in four cities ranging from urban to suburban. PARTICIPANTS: Recruitment was conducted by the clinic's clerical staff or by their care provider. A total of 437 usable T0 surveys and 254 matched and usable T1 surveys were collected. PRIMARY OUTCOME MEASURES: The survey instrument was focused on five core dimensions of patients' primary care experience (accessibility, continuity, comprehensiveness, responsiveness and outcomes of care) as well as on the SF-12 Short-form Health Survey. RESULTS: Scores for all dimensions of patients' primary care experience increased significantly: accessibility (T0=5.9, T1=7.9, p<0.001), continuity (T0=5.5, T1=8.8, p<0.001), comprehensiveness (T0=5.6, T1=8.4, p<0.001), responsiveness (T0=7.2, T1=9.5, p<0.001), outcomes of care (T0=5.0, T1=8.3, p<0.001). SF-12 Physical health T-scores also rose significantly (T0=44.8, T1=47.6, p<0.001) but no changes we found in the mental health T scores (T0=45.8, T1=46.3 p=0.709). CONCLUSIONS: Our results suggest that the NP-led primary care model studied here likely constitutes an effective approach to improve primary care accessibility and quality.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".