Adapting the Quebecois method for assessing implementation to the French National Alzheimer Plan 2008–2012: lessons for gerontological services integration
Bibliographic record
Abstract
INTRODUCTION: Many countries face ageing-related demographic and epidemiological challenges, notably neurodegenerative disorders, due to the multiple care services they require, thereby pleading for a more integrated system of care. The integrated Quebecois method issued from the Programme of Research to Integrate Services for the Maintenance of Autonomy inspired a French pilot experiment and the National Alzheimer Plan 2008-2012. Programme of Research to Integrate Services for the Maintenance of Autonomy method implementation was rated with an evaluation grid adapted to assess its successive degrees of completion. DISCUSSION: The approaching end of the president's term led to the method's institutionalization (2011-2012), before the implementation study ended. When the government changed, the study was interrupted. The results extracted from that 'lost' study (presented herein) have, nonetheless, 'found' some key lessons. KEY LESSONS/CONCLUSION: It was possible to implement a Quebecois integrated-care method in France. We describe the lessons and pitfalls encountered in adapting this evaluation tool. This process is necessarily multidisciplinary and requires a test phase. A simple tool for quantitative assessment of integration was obtained. The first assessment of the tool was unsatisfactory but requires further studies. In the meantime, we recommend using mixed methodologies to assess the services integration level.
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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 | 0.114 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".