Benefits and Challenges Faced by a Nurse Practitioner Working in an Interprofessional Setting in Rural Alberta
Bibliographic record
Abstract
The study aim was to determine benefits and challenges of a community initiative to introduce the nurse practitioner (NP) role in rural primary care. We used a mixed-methods, participatory action research design. Data collection included surveys, interviews, patient record data and shadow billing data. Patient, physician and healthcare professional (HCP) surveys were followed by interviews of survey participants and key local informants. Descriptive statistics were used to summarize survey and patient record data, and content analysis was used to analyze interview data. Benefits, challenges and recommendations were the result of mixed-methods synthesis. Forty-one patients, one HCP and four physicians returned surveys, with 14 patients, one HCP, two local leaders and the NP participating in interviews. The NP provided primary care to 10% of clinic patients in a flexible service delivery model. A high proportion of patients had chronic health concerns. Patient outcomes were generally positive, and patients expressed satisfaction with care. Being connected to the community was important to role implementation. Benefits included increased access to cost-effective, quality primary care. Challenges were funding, limited role understanding and potential for role isolation. Recommendations highlight the need for local community buy-in and provincial support in sustaining the innovation.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".