Médecins omnipraticiens : pratiques et intégration des soins en santé mentale au Québec
Bibliographic record
Abstract
This article examines the socio-demographic profile of general practitioners (GPs), their role in the management of (transient/moderate, severe/chronic) mental health disorders in different areas (urban, semi-urban, and rural) of Quebec as well as if their clinical practice and collaboration are oriented towards integration of mental health services. This crosswise study is based on 398 GPs representative of all Quebec GPs who answered a questionnaire. The study shows that GPs play a central role in mental health. According to territories, they have different socio-demographic and practice profiles. The types of territory and the degree of severity of mental health illnesses influence the propensity of GPs to integrate mental health care. Finally, GPs practiced mostly in silo, but they support greater integration of mental health services. The authors conclude that to improve mental health services integration, more proactive incentives should be favoured by political elites, adapted to the severity of the case and environments (urban, semi-urban or rural). However, the shortage of resources that is particularly striking in rural areas as well as inadequate mechanisms for clinical decision, reduce inter-relations and seriously limit the integration of healthcare.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".