Quebec Eastern Township Regional Dementia Care Update after COVID. Reorganisation for the Quebec Ministerial Alzheimer program
Bibliographic record
Abstract
Abstract Background In the province of Quebec, Canada, primary care management of Alzheimer’s disease is provided by the Family Medicine Group with the support of the Ministerial Initiative on Alzheimer’s Disease and the collaboration of specialized memory clinics. The COVID years have severely strained health resources in all areas, including cognitive intervention resources. We describe the reorganization of regional support in the Eastern Townships. Method The results of a survey on the situation of care are shared with each of the family medicine groups involved in the Eastern Townships Alzheimer Plan, comparing the processes and results of each team to the provincial average. The difficulties and improvements proposed by each of the groups are noted and discussed with a view to continuous improvement. Result A regional tour was necessary to re‐establish contact between the regional support resources (nurse and doctors of the memory clinic) and the Family Medicine Group. Nursing, medical, and pharmaceutical staff and social workers were met at each FMG. Interdisciplinary care models, local resources, leaderchip and development opportunities were discussed. Training needs have been identified. Demands to involve social workers and pharmacists are growing. The evolution of the clientele is striking, with a significant increase in the number of evaluations for patients with mild neurocognitive disorders. Conclusion The presence of at least one experienced person at each FMG made it possible to reorganize the clinics after the staff changes related to the COVID period. The tour allows us to recreate the links that are essential to the development of expertise and to provide specialized support to the family medicine groups involved in the Alzheimer’s plan.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".