Diagnostic, design and implementation of an integrated model of care in France: a bottom-up process with a continuous leadership
Bibliographic record
Abstract
BACKGROUND: Sustaining integrated care is difficult, in large part because of problems encountered securing the participation of health care and social service professionals and, in particular, general practitioners (GPs). PURPOSE: To present an innovative bottom-up and pragmatic strategy used to implement a new integrated care model in France for community-dwelling elderly people with complex needs. RESULTS: In the first step, a diagnostic study was conducted with face-to-face interviews to gather data on current practices from a sample of health and social stakeholders working with elderly people. In the second step, an integrated care model called Coordination Personnes Agées (COPA) was designed by the same major stakeholders in order to define its detailed characteristics based on the local context. In the third step, the model was implemented in two phases: adoption and maintenance. This strategy was carried out by a continuous and flexible leadership throughout the process, initially with a mixed leadership (clinician and researcher) followed by a double one (clinician and managers of services) in the implementation phase. CONCLUSION: The implementation of this bottom-up and pragmatic strategy relied on establishing a collaborative dynamic among health and social stakeholders. This enhanced their involvement throughout the implementation phase, particularly among the GPs, and allowed them to support the change practices and services arrangements.
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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 | 0.042 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".