Ethical Considerations at the Intersection of Social Media and Dementia Prevention Research
Bibliographic record
Abstract
BACKGROUND: Ethical social media use underpins effective online engagement for dementia prevention research. Existing social media guidelines are broad and lack empirical justification reflecting the values and priorities of the dementia community and the challenges specific to prevention research. OBJECTIVES: By engaging professional and community experts, we sought to identify the ethical issues, motivators, and barriers pertaining to social media engagement for dementia prevention research. DESIGN: Semi-structured, qualitative interviews conducted online. SETTING: We recruited participants using a combination of accessible online databases, advertisements/posters through organizational newsletters and websites, social media, registries, and from our network of colleagues. PARTICIPANTS: Professional experts working in dementia research (n=15; e.g., researchers, coordinators) and experts with lived experience (n=14). Experts were from Canada, the USA, the UK, and Chile. MEASUREMENTS: Discussions were analyzed using thematic qualitative analysis methods. RESULTS: Professional experts revealed a dearth of social media guidelines for prevention research, relying on informal sources to supplement ethics board approval. They sought methods of strategic communication for public dialogue (e.g., misinformation, criticism). Experts by experience appreciated the educational benefits of social media but raised risks such as diminished online privacy, dementia-related stigma, being targeted for predatory practices, and misinformation. Various digital inequities (e.g., age, socioeconomic status) dampen social media's reach to diverse publics. Participants acknowledged that younger aging populations have more digital fluency and may benefit more from social media research engagement. CONCLUSIONS: 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 co-creation of ethical 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 | 0.390 | 0.362 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.093 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".