The Collaborative Care Model for Patients With Both Mental Health and Medical Conditions Implemented in Hospital Outpatient Care Settings
Bibliographic record
Abstract
With the increased concern regarding the negative impact that care in silos has on patients and the health care system, there is growing interest in integrated models of care especially for individuals with co-occurring physical and mental health conditions. Although generally applied in a community setting, we adapted and implemented an evidence-based integrated model of care, the collaborative care model (CCM) in an adult and a pediatric hospital-based outpatient clinic. Enrolment was criteria based and management was measurement driven. The model is team based and consists of new roles for its members including the patient, the care manager, the primary care clinician, and the psychiatric consultant. A key role was that of the care manager who worked with the patient and engaged primary care. The care manager also organized team-based treatment planning in systematic case reviews that contributed to the care plan. Support for training of the new and changes in roles is underscored. In this communication we comment on our initial experience of applying the CCM to the hospital outpatient setting.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".