A qualitative study of perceived needs and factors associated with the quality of care for common mental disorders in patients with chronic diseases: the perspective of primary care clinicians and patients
Bibliographic record
Abstract
BACKGROUND: The prevalence of comorbid anxiety and depressive disorders is high among patients with chronic diseases in primary care, and is associated with increased morbidity and mortality rates. The detection and treatment of common mental disorders in patients with chronic diseases can be challenging in the primary care setting. This study aims to explore the perceived needs, barriers and facilitators for the delivery of mental health care for patients with coexisting common mental disorders and chronic diseases in primary care from the clinician and patient perspectives. METHODS: In this qualitative descriptive study, we conducted semi-structured interviews with clinicians (family physician, nurse, psychologist, social worker; n = 18) and patients (n = 10) from three primary care clinics in Quebec, Canada. The themes explored included clinician factors (e.g., attitudes, perception of roles, collaboration, management of clinical priorities) and patient factors (e.g., needs, preferences, access to care, communication with health professionals) associated with the delivery of care. Qualitative data analysis was conducted based on an interactive cyclical process of data reduction, data display and conclusion drawing and verification. RESULTS: Clinician interviews highlighted a number of needs, barriers and enablers in the provision of patient services, which related to inter-professional collaboration, access to psychotherapy, polypharmacy as well as communication and coordination of services within the primary care clinic and the local network. Two specific facilitators associated with optimal mental health care were the broadening of nurses' functions in mental health care and the active integration of consulting psychiatrists. Patients corroborated the issues raised by the clinicians, particularly in the domains of whole-person care, service accessibility and care management. CONCLUSIONS: The results of this project will contribute to the development of quality improvement interventions to increase the uptake of organizational and clinical evidence-based practices for patients with chronic diseases and concurrent common mental disorders, in priority areas including collaborative care, access to psychotherapy and linkages with specialized mental health care.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".