Identifying research priorities for conducting dementia‐related research with ethno‐racial communities: Results of a DELPHI study
Bibliographic record
Abstract
BACKGROUND: The population of people living with dementia, globally and within many countries, is becoming increasingly ethno-racially diverse. Yet, research in dementia does not adequately reflect this diversity. Guidance on research priorities can help researchers make decisions about the areas of study on which they want to focus. Engaging people living with dementia and family/friend care partners - particularly those who are part of ethno-racial communities - is critical to ensuring that the research priorities identified align with the needs and preferences of individuals impacted by dementia who are from ethno-racial communities. Thus, a DELPHI study with a diverse group of participants was undertaken to identify research priorities in dementia and ethno-racial diversity. METHOD: Multiple strategies were used to identify the items to include in the DELPHI surveys and the members of the DELPHI panel. Survey items: We gathered suggestions at a symposium on dementia and ethno-racial diversity. Attendees included people living with dementia, care partners, providers who work with people living with dementia and/or people from diverse communities, and researchers. These suggestions were supplemented with gaps identified in the literature. The generated items (N = 45) were grouped into 9 categories. DELPHI panel: participants in previous projects related to diversity were invited to participate. Snowball sampling was used to identify additional members. In total, 74 individuals were invited to participate. Three rounds of surveys were administered with participants rating the importance of each item (1=not important, 5=extremely important). In the final round, participants identified their top 5 most important research questions and 2 most important categories of research questions. RESULT: Average ratings in Round 1 ranged between 3.71 and 4.78. No items were dropped following Round 1. Average ratings in Round 2 were similar, ranging from 3.47 to 4.84. Most of the top-rated items related to health care and health service use followed by well-being. CONCLUSION: The high ratings of importance across items suggests that the significant gaps in this area makes differentiating among priorities challenging. Nevertheless, the identified research priorities can inform researchers interested in research on dementia and ethno-racial diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.140 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".