Collaboration between general practitioners (GPs) and mental healthcare professionals within the context of reforms in Quebec.
Bibliographic record
Abstract
Background In the context of the high prevalence and impact of mental disorders worldwide, and less than optimal utilisation of services and adequacy of care, strengthening primary mental healthcare should be a leading priority. This article assesses the state of collaboration among general practitioners (GPs), psychiatrists and psychosocial mental healthcare professionals, factors that enable and hinder shared care, and GPs' perceptions of best practices in the management of mental disorders. A collaboration model is also developed. Methods The study employs a mixed-method approach, with emphasis on qualitative investigation. Drawing from a previous survey representative of the Quebec GP population, 60 GPs were selected for further investigation. Results Globally, GPs managed mental healthcare patients in solo practice in parallel or sequential follow-up with mental healthcare professionals. GPs cited psychologists and psychiatrists as their main partners. Numerous hindering factors associated with shared care were found: lack of resources (either professionals or services); long waiting times; lack of training, time and incentives for collaboration; and inappropriate GP payment modes. The ideal practice model includes GPs working in multidisciplinary group practice in their own settings. GPs recommended expanding psychosocial services and shared care to increase overall access and quality of care for these patients. Conclusion As increasing attention is devoted worldwide to the development of optimal integrated primary care, this article contributes to the discussion on mental healthcare service planning. A culture of collaboration has to be encouraged as comprehensive services and continuity of care are key recovery factors of patients with mental disorders.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".