Assessing care models implemented in primary healthcare for persons with dementia: a mixed-methods study protocol
Bibliographic record
Abstract
INTRODUCTION: Dementia is on the rise in Canada and globally. Ensuring accessibility to diagnosis, treatment and management throughout the course of the disease is a very significant problem worldwide. In order to provide comprehensive care to patients and their caregivers, enhancing primary care-based dementia care is seen as the way forward. In many Canadian provinces various collaborative care models (collCMs) anchored in primary care to improve dementia care have been developed and implemented. The overall objective of our research programme is to identify key factors for the successful implementation of collCMs, and to facilitate dissemination and scale-up of dementia best practices. METHODS AND ANALYSIS: We will use a convergent mixed-methods design. An observational study using chart review (2014-2016) and questionnaires (2014-2018; repeated in 2020) will measure application of guidelines and implementation of collCMs. This study will be complemented with a qualitative descriptive study using interviews (2017-2020) conducted in parallel. Quantitative and qualitative results will be further integrated using a matrix representing sites and findings. An integrated knowledge exchange strategy will ensure uptake by principal stakeholders throughout the research. ETHICS AND DISSEMINATION: Our study has been approved by all relevant ethics committees. Our dissemination plan follows an integrated knowledge transfer strategy using provincial, national and international councils. We will present the results individually to the clinical sites and then to these councils. Our research will be the first provincial and cross jurisdictional evaluation of primary care models for patients living with dementia, providing evidence on the ongoing debate on the respective role of clinicians in primary care and specialists in caring for patients 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 | 0.093 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".