Variables associated with general practitioners taking on serious mental disorder patients
Bibliographic record
Abstract
BACKGROUND: As part of community-based initiatives to strengthen integrated care and promote patient recovery, GPs are asked to play a greater part in treating serious mental disorder (SMD) patients. All current healthcare reforms favour the reinforcement of primary care. More information on enhancing the role of GPs in mental health would benefit policymakers, especially as regards SMD patients, where little research has been published as yet. This article assesses variables associated with GPs taking on SMD patients. METHODS: The study, encompassing multiple sites, is based on a sample of 398 GPs, representative of the GP population in the Canadian province of Quebec. GPs were asked to answer a 143-item questionnaire on their socio-demographic and clinical practice profiles, patient characteristics, perceived inter-professional relationships and quality of care. Descriptive, bivariate and multivariate analyses were performed. RESULTS: Our data highlighted that GPs currently followed up only a minority of SMD patients on a continuous basis and far fewer for both physical and mental health problems. A linear regression model that accounts for 43% of the variance was generated. The best variables associated positively with GPs taking on SMD patients were: frequency of referrals for joint follow-up with other resources, and involvement in post-hospitalization follow-up. Conversely, lack of expertise in mental health (related in our model to frequency of mental disorder patient transfer due to insufficient mental health training) is associated with a lower incidence of GPs taking on patients. CONCLUSION: As advocated in current healthcare reforms, our study confirms the need to promote greater GP involvement in integrated care models and enhance their training in mental health--thereby helping to reverse the trend among GPs of transferring SMD patients to specialized care. Patients with stable SMDs ought to have the same care access as the general population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".