Choosing Healthcare Options by Involving Canada's Elderly: a protocol for the CHOICE realist synthesis project on engaging older persons in healthcare decision-making
Bibliographic record
Abstract
INTRODUCTION: While patient and citizen engagement has been recognised as a crucial element in healthcare reform, limited attention has been paid to how best to engage seniors-the fastest growing segment of the population and the largest users of the healthcare system. To improve the healthcare services for this population, seniors and their families need to be engaged as active partners in healthcare decision-making, research and planning. This synthesis aims to understand the underlying context and mechanisms needed to achieve meaningful engagement of older adults in healthcare decision-making, research and planning. METHODS AND ANALYSIS: The CHOICE Knowledge Synthesis Project: Choosing Healthcare Options by Involving Canada's Elderly aims to address this issue by synthesising current knowledge on patient, family, and caregiver engagement. A realist synthesis will support us to learn from other patient and citizen engagement initiatives, from previous research, and from seniors, families and caregivers themselves. The synthesis will guide development or adaptation of a framework, leading to the development of best practice guidelines and recommendations for engagement of older people and their families and caregivers in clinical decision-making, healthcare delivery, planning and research. ETHICS AND DISSEMINATION: The components of this protocol involving consultation with patients or caregivers have received ethics clearance from the University of Waterloo, Office of Research Ethics (ORE#19094). After completion of the project, we will amalgamate the information collected into a knowledge synthesis report which will include best practice guidelines and recommendations for patient, family and caregiver engagement in clinical and health system planning and research contexts. RESULTS: Will be further disseminated to citizens, clinicians, researchers and policymakers with the help of our partners. TRIAL REGISTRATION NUMBER: CRD42015024749.
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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 | 0.169 | 0.168 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.076 | 0.010 |
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".