Practical social media recommendations for dementia prevention researchers
Bibliographic record
Abstract
INTRODUCTION: Practical social media recommendations are needed to facilitate greater engagement in dementia prevention research. Alongside relevant experts, our aim was to develop a set of consensus recommendations that reflect the values and priorities of prospective participants to guide social media use. METHODS: = 6) experiences. Consensus was defined a priori as ≥ 70% agreement. RESULTS: Twenty-six items achieved consensus. Two items reached consensus in round 1: ethical considerations of closed social media groups (88%) and of social media users sharing prevention content with connections who are not on social media (79%). Nine items reached consensus in round 2, related to misinformation (79%), stigma (93%), and other key aspects of social media communication. After revisions, 15 items reached consensus in the final round. These items included: identifying when researchers ought to engage, managing closed social media groups, rankings of short form content, prioritizing lay summaries and multimedia resources, and rankings of preferred language. One item about the language of prevention for audiences living with dementia did not reach consensus. Final consensus items formed the new set of recommendations, which we organized into seven social media use cases. These use cases include setting up a social media page or community, 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. DISCUSSION: These consensus recommendations can help dementia prevention researchers harness social media use for the purposes of public engagement and uphold the norms and values specific to the dementia research and broader communities. Highlights: We created social media recommendations with research and community experts.Recommendations cover key ethical considerations for dementia prevention research.Areas include misinformation, stigma, information updates, and preferred language.Full consensus recommendations are organized into seven social media use cases.
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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 | 0.101 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.043 | 0.024 |
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".