Establishing a dementia care competency framework for care partners, health and social care providers: A modified Delphi study protocol
Bibliographic record
Abstract
Dementia care requires a wide range of knowledge and skills delivered by both unpaid care partners and health and social care providers. In Ontario, Canada, no systematic framework currently aligns educational content with dementia care competencies. This gap risks the effectiveness of dementia-related education and care delivery. Therefore, this study protocol describes our approach to achieve consensus on the behavioural statements that describe the core competencies required of care partners, health and social care providers involved in dementia care. We will use a two-round modified Delphi method with expert panellists from two groups: (1) care partners with experience caring for someone living with dementia and (2) interprofessional health and social care providers working with people living with dementia. We will purposively recruit up to 80 panellists (40 per group). Panellists will assess standardized behavioural competency statements derived from earlier study phases, rating them on importance and measurability using a nine-point Likert scale. Round 1 will include opportunities for panellists to suggest new statements. Statements reaching ≥70% agreement (rated 7-9 on a 9-point Likert scale) and demonstrating a narrow interquartile range (IQR ≤ 2) will advance to Round 2. In the second round, a higher consensus threshold (≥80%) and stability in ratings (median shift ≤1 point) will determine final inclusion. Qualitative feedback through open-ended questions will be analyzed alongside quantitative results to refine the statements. Findings will support the development of a consensus-based Dementia Care Competency Framework to guide evidence-based educational initiatives and care delivery across settings. This inclusive approach will provide a model for ensuring both lived experience and clinical expertise shape the future of dementia care education in Canada and beyond.
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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 | 0.095 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.017 |
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".