Perspectives of Quebec Primary Health Care Nurse Practitioners on Their Role and Challenges in Chronic Disease Management: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Primary health care nurse practitioners (PHCNPs) can play a key role in chronic disease management. However, little is known about the challenges they face. PURPOSE: The study aimed to describe PHCNPs' perspectives on their role for patients with chronic health conditions, the barriers they face, and facilitating factors. METHODS: A qualitative descriptive exploratory study was conducted with 24 PHCNPs in the Canadian province of Quebec. RESULTS: PHCNPs believe that they are in an optimal position to address the needs of patients with chronic health conditions, especially in providing self-management support. However, PHCNPs reported feeling pressured to practice according to a biomedical model and to constantly defend their role in chronic disease management. They feel that they are frequently being diverted from their role to compensate for the lack of family doctors. PHCNPs made concrete recommendations to optimize their autonomous practice and quality of care: promoting strong interprofessional communication skills, genuine mentoring relationships between PHCNPs and partner physicians, managers upholding the full scope of PHCNPs' practice, and a more flexible legislative framework. CONCLUSIONS: The original conception of PHCNPs as health professionals with unique characteristics is at stake. The factors that should be targeted to support the autonomy of PHCNPs were identified.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".