GP group profiles and involvement in mental health care
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Mental health is one of the leading causes of morbidity worldwide. Its impact in terms of cost and loss of productivity is considerable. Improving the efficiency of mental health care system has thus been a high priority for decision makers. In the context of current reforms that privilege the reinforcement of primary mental health care and integration of services, this article brings new lights on the role of general practitioners (GPs) in managing mental health, and shared-care initiatives developed to deal with more complex cases. The study presents a typology of GPs providing mental health care, by identifying clusters of GP profiles associated with the management of patients with common or serious mental disorders (CMD or SMD). METHODS: GPs in Quebec (n = 398) were surveyed on their practice, and socio-demographic data were collected. RESULTS: Cluster analysis generated five GP profiles, including three that were closely tied to mental health care (labelled, respectively: group practice GPs, traditional pro-active GPs and collaborative-minded GPs), and two not very implicated in mental health (named: diversified and low-implicated GPs, and money-making GPs). CONCLUSION: The study confirmed the central role played by GPs in the treatment of patients with CMD and their relative lack of involvement in the care of patients with SMD. Study results support current efforts to strengthen collaboration among primary care providers and mental health specialists, reinforce GP training, and favour multi-modal clinical and collaborative strategies in 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".