The association between the level of institutional support for dementia care in primary care practices and the quality of dementia primary care: A retrospective chart review
Bibliographic record
Abstract
INTRODUCTION: Institutional support, encompassing financial and training support, as well as interdisciplinary teams, may be important for the quality of dementia primary care for persons living with dementia. The aim of this study was to measure the association between the level of institutional support provided to primary care practices and the quality of dementia care. METHODS: This was a cross-sectional chart review in 33 Canadian primary care practices to measure the quality of dementia primary care using a quality of follow-up score. The score was based on the assessment of 10 indicators. Practices were chosen using a purposeful sampling method with varying levels of institutional support for dementia primary care (e.g., financial support, training, interdisciplinary team). A linear mixed-effect model was used to measure the association between the level of institutional support and the quality of dementia care. RESULTS: There was a significant association between the level of institutional support and the quality of dementia care (mean difference = 23.5, 95% confidence interval: 16.4, 30.6). DISCUSSION: Providing more institutional support for primary care practices could be a promising avenue to improve the care of persons living with dementia.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".