Bibliographic record
Abstract
Up to 50% of patients seen in primary care have mental health problems, the severity and duration of their problems often being similar to those of individuals seen in the specialized sector. This article describes the reasons, advantages, and challenges of collaborative or shared care between primary and mental health teams, which are similar to those of consultation-liaison psychiatry. In both settings, clinicians deal with the complex interrelationships between medical and psychiatric disorders. Although initial models emphasized collaboration between family physicians, psychiatrists, and nurses, collaborative care has expanded to involve patients, psychologists, social workers, occupational therapists, pharmacists, and other providers. Several factors are associated with favorable patient outcomes. These include delivery of interventions in primary care settings by providers who have met face-to-face and/or have pre-existing clinical relationships. In the case of depression, good outcomes are particularly associated with approaches that combined collaborative care with treatment guidelines and systematic follow-up, especially for those with more severe illness. Family physicians with access to collaborative care also report greater knowledge, skills, and comfort in managing psychiatric disorders, even after controlling for possible confounders such as demographics and interest in psychiatry. Perceived medico-legal barriers to collaborative care can be addressed by adequate personal professional liability protection on the part of each practitioner, and ensuring that other health care professionals with whom they work collaboratively are similarly covered.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".