Using Co-creation Focus Groups to Customise a Remote Multidomain Programme Designed to Increase Dementia Literacy
Bibliographic record
Abstract
Objectives To adapt the content and functionalities of Brain Health PRO, a web-based multidomain program designed to increase dementia literacy, to the context and needs of users, providers and community organisations across Québec, Canada. Design Five consecutive qualitative co-creation focus group sessions 30–90 min in duration each, exploring potential barriers and facilitators to usability, accessibility, comprehensibility, participant recruitment and retention. Setting Virtual meetings. Participants A 15-member team based in Québec and Ontario, Canada, consisting of 9 researchers (including a graduate student and the project coordinator), representing occupational therapy, sensory rehabilitation, neuropsychology, psychology, health science and research methods, 3 informal caregivers of older adults living with cognitive decline and 3 members of the Federation of Quebec Alzheimer Societies. Data analysis Session recordings were summarised through both qualitative description and thematic analysis. Results The synthesised recommendations included adjustments around diversity, the complexity and presentation styles of the materials, suggestions on refining the web interface and the measurement approaches; it influenced aspects of participant recruitment, retention efforts and engagement with the content of Brain Health PRO. Conclusions Co-creation in dementia prevention research is important because it involves collaboration between researchers, community support and service providers, and persons with lived experience as care providers, in the design and implementation of clinical studies. This approach helps to ensure that the content and presentation of educational material is relevant and meaningful to the target population and those involved in its delivery, and it leads to a greater understanding of their needs and perspectives.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".