Improving dementia care: insights from audit and feedback in interdisciplinary primary care sites
Bibliographic record
Abstract
BACKGROUND: Many primary care sites have implemented models to improve detection, diagnosis, and management of dementia, as per Canadian guidelines. The aim of this study is to describe the responses of clinicians, managers, and staff of sites that have implemented these models when presented with audit results, their insights on the factors that explain their results, their proposed solutions for improvement and how these align to one another. METHODS: One audit and feedback cycle was carried out in eight purposefully sampled sites in Ontario, Canada, that had previously implemented dementia care models. Audit consisted of a) chart review to assess quality of dementia care indicators, b) questionnaire to assess the physicians' knowledge, attitudes and practice toward dementia care, and c) semi-structured interviews to understand barriers and facilitators to implementing these models. Feedback was given to clinicians, managers, and staff in the form of graphic and oral presentations, followed by eight focus groups (one per site). Discussions revolved around: what audit results elicited more discussion from the participants, 2) their insights on the factors that explain their audit results, and 3) solutions they propose to improve dementia care. Deductive content and inductive thematic analyses, grounded in causal pathways models' theory was performed. FINDINGS: The audit and feedback process allowed the 63 participants to discuss many audit results and share their insights on a) organizational factors (lack of human resources, the importance of organized links with community services, clear roles and support from external memory clinics) and b) clinician factors (perceived competency practice and attitudes on dementia care), that could explain their audit results. Participants also provided solutions to improve dementia care in primary care (financial incentives, having clear pathways, adding tools to improve chart documentation, establish training on dementia care, and the possibility of benchmarking with other institutions). Proposed solutions were well aligned with their insights and further nuanced according to contextual details. CONCLUSIONS: This study provides valuable information on solutions proposed by primary care clinicians, managers, and staff to improve dementia care in primary care. The solutions are grounded in clinical experience and will inform ongoing and future dementia strategies.
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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 | 0.068 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".