Secondary Care Clinic for Chronic Disease: Protocol
Bibliographic record
Abstract
BACKGROUND: The complexity of chronic disease management activities and the associated financial burden have prompted the development of organizational models, based on the integration of care and services, which rely on primary care services. However, since the institutions providing these services are continually undergoing reorganization, the Centre hospitalier affilié universitaire de Québec wanted to innovate by adapting the Chronic Care Model to create a clinic for the integrated follow-up of chronic disease that relies on hospital-based specialty care. OBJECTIVE: The aim of the study is to follow the project in order to contribute to knowledge about the way in which professional and management practices are organized to ensure better care coordination and the successful integration of the various follow-ups implemented. METHODS: The research strategy adopted is based on the longitudinal comparative case study with embedded units of analysis. The case study uses a mixed research method. RESULTS: We are currently in the analysis phase of the project. The results will be available in 2015. CONCLUSIONS: The project's originality lies in its consideration of the macro, meso, and micro contexts structuring the creation of the clinic in order to ensure the integration process is successful and to allow a theoretical generalization of the reorganization of practices to be developed.
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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 | 0.048 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.126 | 0.032 |
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".