[Mental health in primary care: applying psychiatric knowledge to primary care or a whole different practice?]
Bibliographic record
Abstract
BACKGROUND: In Canada and several other countries, large cohorts of patients who used to be followed in psychiatric clinics are now steered toward primary care. To support this new reality, measures have been taken and investments made in service organization, collaborative work arrangements, and teaching geared toward primary care clinicians. However, these initiatives were implemented when little was known about GPs' needs. METHODS: Using a qualitative approach, we analyzed the content of GPs' statements to explore when, why, and for what concerns GPs are inclined to collaborate with or seek advice from psychiatrists. RESULTS: The results provide an innovative understanding of their practice and its boundaries and suggest that the management of patients with mental health problems in primary care is actually very different from what is done in psychiatry. CONCLUSION: Uncertainty about the broad spectrum of what is normal, the longitudinal aspect of the relationship, and the proximity of the soma are among the specificities we found that could be helpful in organizing care and educating primary care clinicians and medical students more coherently and efficiently.
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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 | 0.005 | 0.018 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".