Management of mental health problems by general practitioners in Quebec.
Bibliographic record
Abstract
OBJECTIVE: To document the management of mental health problems (MHPs) by general practitioners. DESIGN: A mixed-method study consisting of a self-administered questionnaire and qualitative interviews. An analysis was also performed of Régie de l'assurance maladie du Québec administrative data on medical procedures. SETTING: Quebec. PARTICIPANTS: Overall, 1415 general practitioners from different practice settings were invited to complete a questionnaire; 970 general practitioners were contacted. A subgroup of 60 general practitioners were contacted to participate in interviews. MAIN OUTCOME MEASURES: The annual frequency of consultations over MHPs, either common (CMHPs) or serious (SMHPs), clinical practices, collaborative practices, factors that either support or interfere with the management of MHPs, and recommendations for improving the health care system. RESULTS: The response rate was 41% (n = 398 general practitioners) for the survey and 63% (n = 60) for the interviews. Approximately 25% of visits to general practitioners are related to MHPs. Nearly all general practitioners manage CMHPs and believed themselves competent to do so; however, the reverse is true for the management of SMHPs. Nearly 20% of patients with CMHPs are referred (mainly to psychosocial professionals), whereas nearly 75% of patients with SMHPs are referred (mostly to psychiatrists and emergency departments). More than 50% of general practitioners say that they do not have any contact with resources in the mental health field. Numerous factors influence the management of MHPs: patients' profiles (the complexity of the MHP, concomitant disorders); individual characteristics of the general practitioner (informal network, training); the professional culture (working in isolation, formal clinical mechanisms); the institutional setting (multidisciplinarity, staff or consultant); organization of services (resources, formal coordination); and environment (policies). CONCLUSION: The key role played by general practitioners and their support of the management of MHPs were evident, especially for CMHPs. For more optimal management of primary mental health care, multicomponent strategies, such as shared care, should be used more often.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".