Factors and Strategies Influencing Integrated Self‐Management Support for People With Chronic Diseases and Common Mental Disorders: A Qualitative Study of Canadian Primary Care Nurses' Experience
Bibliographic record
Abstract
AIM: To describe the factors influencing clinical integration of self-management support by primary care nurses for people with physical chronic diseases and common mental disorders, as well as strategies for improvement. DESIGN: Thorne's interpretive descriptive qualitative approach. METHODS: Semi-structured interviews lasting from 60 to 90 min were carried out virtually with nurses from Family Medicine Groups and University Family Medicine Groups across the province of Quebec (Canada) from January 2022 to January 2023. Twenty-three primary care nurses were recruited through purposive and snowball sampling from three networks. Iterative deductive and inductive thematic analysis was completed using Valentijn's Rainbow Model of Integrated Care. RESULTS: The study identified several factors influencing integrated self-management support from primary care nurses across integration domains: clinical (knowledge, skills, training and experience; workload; approaches and activities; attitudes and behaviours; clinical tools), professional (interprofessional and nursing roles; collaboration; team composition), normative and functional (culture and organisational mechanisms). Improvement strategies pointed to the necessity of developing training regarding common mental disorders, adapted clinical tools, clinical support and coaching through collaboration and culture change. CONCLUSION: These findings suggest that a cultural shift emphasising continuous improvement through targeted training and coaching is essential to enhance integrated self-management support. Identifying factors and improvement strategies will help implement future interventions and tailor current practices. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Identifying barriers and facilitators, along with proposing improvement strategies, will enable the implementation of more effective interventions and the adaptation of care practices to better support self-management. Additionally, it will influence stakeholders to modify the context surrounding integrated self-management support and interprofessional practise. REPORTING METHOD: Consolidated criteria for reporting qualitative research (COREQ). PATIENT AND PUBLIC CONTRIBUTION: No patient or public contribution.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".