Evaluating the co‐design of a program to supporting living well with dementia
Bibliographic record
Abstract
Abstract Background Engaged research involves stakeholders as active partners in the research process, not just as research participants. Engaged dementia research can improve the relevance and quality of research, empower persons living dementia (PLWD) and family care partners, and acknowledge their expertise gained through lived experience. Conducting engaged research is challenging during COVID‐19 when in‐person interactions are difficult or impossible. The Dementia Lifestyle Intervention for Getting Health Together (DELIGHT) project used an online, engaged research approach to co‐design the DELIGHT program, a 12‐week program to support the health and well‐being goals of PLWD and care partners. Here, we used mixed‐methods to evaluate the DELIGHT co‐design process, its alignment with an Authentic Partnership approach, and experiences, satisfaction, and suggestions for future online co‐design processes. Method The DELIGHT project engaged 29 people in the co‐design process during Summer 2020, including PLWD, care partners, service/program providers, healthcare providers, and researchers. The co‐design team met over six, 1.5‐hour online meetings (via Zoom) to develop the program. Although the co‐design process is on‐going, evaluation focused on this first phase of the co‐design process. A 17‐item survey assessed alignment with an Authentic Partnerships approach. Focus groups and interviews used a semi‐structured guide to assess experiences, satisfaction, and suggestions for future on‐line co‐design. Result Surveys were completed by 11 co‐design members, and 11 co‐design members participated in interviews or focus groups (not all completed both). Initial analysis of survey results indicates co‐design members felt respected and safe in the co‐design process, felt their perspectives and opinions were valued, and valued the contributions of other members (100% agreed or strongly agreed). All survey respondents felt they gained knowledge and strategies for living well with dementia during the co‐design process. All survey respondents also strongly agreed that the DELIGHT program, when implemented, will have a positive impact on PLWD and care partners. Analysis of interviews/focus groups is ongoing and will be available in Spring 2021. Conclusion Co‐design members expressed high satisfaction with the process and felt their experiences were valued and heard. All members expressed a strong belief that the program developed will benefit PLWD and care partners.
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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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".