Ethical social media use for dementia prevention research: Perspectives of research professionals and community members
Bibliographic record
Abstract
Abstract Background Ethical social media use underpins effective online engagement for dementia prevention research. Current social media guidelines are broad and lack empirical justification reflecting the values and priorities of the dementia community. By engaging professional and community experts, we seek to identify the ethical parameters of using social media for dementia prevention research. Method We conducted semi‐structured, qualitative interviews with professional experts working in dementia research (n = 15; e.g., researchers, coordinators) and experts by experience (n = 14; e.g., persons with lived experience). Experts were from Canada, the USA, the UK, and South America. Discussions were analyzed using thematic qualitative analysis methods. Result Professional experts revealed a dearth of ethical guidelines when using social media for research engagement, relying on informal sources of guidance to supplement ethics board approval. Areas identified as needing more attention included privacy concerns, handling instances of misinformation and self‐disclosure, the constraints of prescribed language, and moderating public reactions to posts. Experts by experience appreciated the educational benefits of social media for learning about healthy aging. They valued accessible resources but expressed uncertainty on distinguishing between facts and misinformation. Factors enhancing trust of social media content included transparent presentation, traceable sources, relationship‐building, and partnering with community organizations. Having a family history of dementia was a key motivator for engaging on social media. The negative consequences of diminished online privacy, such as stigma or being targeted for predatory practices, were a major ethical concern. Both groups discussed factors that dampen social media’s theoretical reach to diverse publics, such as existing inequalities permeating digital access (i.e., age, socioeconomic, literacy, English fluency, urban/rural) and past violations undermining trust. Nevertheless, participants cited social media’s wide reach as a societal benefit to improve research participation and awareness of dementia prevention. They acknowledged that younger aging populations have more digital fluency and may benefit more from social media research engagement. Conclusion Research professionals and community members identified ethical and contextual factors surrounding the use of social media for dementia prevention, and a need for more guidance. The next project phase will use these data to inform the creation of consensus‐based guidelines for brain health research.
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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 | 0.198 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.032 | 0.055 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".