Opening the Black Box of Clinical Collaboration in Integrated Care Models for Frail, Elderly Patients
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to understand better the clinical collaboration process among primary care physicians (PCPs), case managers (CMs), and geriatricians in integrated models of care. METHODS: We conducted a qualitative study with semistructured interviews. A purposive sample of 35 PCPs, 7 CMs, and 4 geriatricians was selected in 2 integrated models of care for frail elderly patients in Canada and France: System of Integrated Care for Older Patients of Montreal and Coordination of Care for Older Patients of Paris. Data were analyzed using a grounded theory approach. FINDINGS: The dynamics of the collaboration process develop in three phases: (1) initiating relationships, (2) developing real two-way collaboration, and (3) developing interdisciplinary teamwork. The findings suggest that CMs and geriatricians collaborated well from the start and throughout the care management process. Real collaboration between the CMs and the PCPs occurred only later and was mostly fostered by the interventions of the geriatricians. PCPs and geriatricians collaborated only occasionally. IMPLICATIONS: The findings provide information about PCPs' commitment to the integrated models of care, the legitimization of the CM's role among PCPs, and the appropriate positioning of geriatricians in such models.
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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 | 0.035 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".