Co‐creating practical social media recommendations for dementia prevention researchers: A Delphi study
Bibliographic record
Abstract
Abstract Background Social media provides dementia prevention researchers with additional opportunities to engage diverse audiences, including healthy individuals who unaware of their eligibility to take part in dementia‐related research. However, practical social media guidance that reflects the values and priorities of potential participants is needed. To address this gap, we sought to create consensus recommendations with research professionals and community experts. Method We conducted a three‐round, modified Delphi consisting of three online surveys and three conferences calls. Based on data from earlier project phases, a group of 16 experts with lived (n = 10) and professional (n = 6) experiences co‐created a set of recommendations to guide ethical social media use for dementia prevention research. Consensus was defined a priori as ≥70% agreement. Result Twenty‐six items attained panelist agreement. Two privacy‐related items reached consensus in Round 1: the ethical appropriateness of closed social media groups (88%) and accessing individuals not on social media through social media contacts (79%). Nine items reached consensus in Round 2, including addressing misinformation (79%), stigma (93%), and other pertinent items for social media communication (e.g., public criticism, explaining process of science). Fifteen of the sixteen remaining items reached consensus after revision in Round 3. These items included defining appropriate comments (100%), rules of engagement on dementia social media pages (100%, e.g., positive messaging, relevant topics), and ranking appropriate prevention language use for different audiences (e.g., young, healthy adults, individuals with a family history). One item pertaining to language use for people living with dementia did not reach consensus. Recommendations were organized into seven social media use cases: setting up a social media page, handling online misinformation, actively challenging stigma, handling difficult online interactions, introducing new research to the public, help with study recruitment, and the language of prevention when writing posts. Conclusion Research professionals and community members co‐created consensus recommendations to facilitate the ethical use of social media by dementia prevention researchers. By providing practical guidance, these recommendations will uphold ethical decision‐making on social media. Next steps are to create an evaluation tool and distribute the recommendations to relevant audiences.
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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 | 0.159 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| 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".