Primary care nursing activities with patients affected by physical chronic disease and common mental disorders: a qualitative descriptive study
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To describe nursing activities in primary care with patients affected by physical chronic disease and common mental disorders. BACKGROUND: Patients in primary care who are affected by physical chronic disease and common mental disorders such as anxiety and depression require care and follow-up based on their physical and mental health condition. Primary care nurses are increasingly expected to contribute to the care and follow-up of this growing clientele. However, little is known about the actual activities carried out by primary care nurses in providing this service in the Province of Quebec (Canada). METHODS: A qualitative descriptive study was conducted. Data were obtained through semistructured individual interviews with 13 nurses practising among patients with physical chronic disease in seven Family Medicine Groups in Quebec (Canada). RESULTS: Participants described five activity domains: assessment of physical and mental health condition, care planning, interprofessional collaboration, therapeutic relationship and health promotion. CONCLUSION: The full potential of primary care nurses is not always exploited, and some activities could be improved. Evidence for including nurses in collaborative care for patients affected by physical chronic disease and common mental disorders has been shown but is not fully implemented in Family Medicine Groups. Future research should emphasise collaboration among mental health professionals, primary care nurses and family physicians in the care of patients with physical chronic disease and common mental disorders. RELEVANCE TO CLINICAL PRACTICE: Primary care nurses would benefit from gaining more knowledge about common mental disorders and from identifying the resources they need to contribute to managing them in an interdisciplinary team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".