Utilizing Social Media to Study Information-Seeking and Ethical Issues in Gene Therapy
Bibliographic record
Abstract
BACKGROUND: The field of gene therapy is rapidly evolving, and while hopes of treating disorders of the central nervous system and ethical concerns have been articulated within the academic community, little is known about views and opinions of different stakeholder groups. OBJECTIVE: To address this gap, we utilized social media to investigate the kind of information public users are seeking about gene therapy and the hopes, concerns, and attitudes they express. METHODS: We conducted a content analysis of questions containing the keywords "gene therapy" from the Q&A site "Yahoo! Answers" for the 5-year period between 2006 and 2010. From the pool of questions retrieved (N=903), we identified those containing at least one theme related to ethics, environment, economics, law, or society (n=173) and then characterized the content of relevant answers (n=399) through emergent coding. RESULTS: The results show that users seek a wide range of information regarding gene therapy, with requests for scientific information and ethical issues at the forefront of enquiry. The question sample reveals high expectations for gene therapy that range from cures for genetic and nongenetic diseases to pre- and postnatal enhancement of physiological attributes. Ethics questions are commonly expressed as fears about the impact of gene therapy on self and society. The answer sample echoes these concerns but further suggests that the acceptability of gene therapy varies depending on the specific application. CONCLUSIONS: Overall, the findings highlight the powerful role of social media as a rich resource for research into attitudes toward biomedicine and as a platform for knowledge exchange and public engagement for topics relating to health and disease.
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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".