Ethical and legal considerations in social media research for health technology assessment: conclusions from a scoping review
Bibliographic record
Abstract
OBJECTIVES: for generating patient insights for the use by health technology assessment (HTA) or health policy decisions. METHODS: A scoping review of the literature was conducted in PubMed and Embase and identified 935 potential references published between January 2017 and June 2021. After title and abstract screening by three reviewers, 40 publications were included, the relevant information was extracted and data were collected in a mind map, which was then used to structure the output of the review. RESULTS: may reveal new insights of relevance to HTA or health policies into patient needs, patient experiences, or patient behaviors. However, the research approaches, methods, data use, interpretation, and communication may expose those who post the data in social media channels to risks and potential harms relating to privacy, anonymity/confidentiality, authenticity, context, and rapidly changing technologies. CONCLUSIONS: An actively engaged approach to ensuring ethical innocuousness is recommended that carefully follows best practices throughout planning, conduct, and communication of the research. Throughout the process and as a follow-up, there should be a discourse with the ethical experts to maximally protect the current and future users of social media, to support their trust in the research, and to advance the knowledge in parallel to the advancement of the media themselves, the technologies, and the research tools.
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 | 0.299 | 0.530 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".