Quality indicator framework for primary care of patients with dementia
Bibliographic record
Abstract
OBJECTIVE: To develop a framework of population-based primary care quality indicators adapted to patients with dementia and to identify a subset of stakeholder-driven priority indicators. DESIGN: Framework development was carried out through the selection of an initial framework based on a rapid review and identification of relevant indicators and enrichment based on existing dementia indicators and guidelines. Prioritization of indicators was carried out through a stakeholder survey. SETTING: Ontario, Quebec, New Brunswick, and Saskatchewan. PARTICIPANTS: Stakeholders in community dementia care (N=109) including clinicians, patients, caregivers, decision makers, and managers. MAIN OUTCOME MEASURES: Primary care quality indicators. RESULTS: The framework comprised 34 indicators across 8 domains of quality (access, integration, effective care, efficient care, equity, safety, population health, and patient-centred care). Access to a regular primary care provider, continuity of care, early-stage diagnosis, and access to home care were consistently rated as priorities. Equitable care was a specific priority among patients and caregivers; clinicians reported avoidable hospitalizations as among their priorities. CONCLUSION: A framework of indicators was established for persons with dementia that adds an important dimension to existing primary care and dementia quality indicators by providing primary care and population-based perspectives. This framework could set a foundation for the ongoing monitoring of primary care practices and policies for persons with dementia at a population 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 | 0.064 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".