Community perspectives on drug promotion on social media during the COVID-19 pandemic in KwaZulu-Natal, South Africa
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, social media and web-based platforms were widely used to promote medicinal substances. To assess community perspectives on drug promotions on social media, we conducted qualitative research using three workshops. The workshops aimed to highlight the public understanding of drug advertising focusing on community perceptions of social media drug promotions, their risks and benefits. Discussions were conducted on the importance of adhering to national drug regulation policies and the World Health Organisation ethical criteria for promotion, advertisement, and publicity of medicines. Methods: Participants for the workshops were purposively sampled from local community youth groups and healthcare facilities. Two workshops included ten young adults aged 18-35, while the third workshop involved three healthcare professionals and one traditional healer. Results: The study participants' highlighted the value of honesty and trust in the drug promotions. Gaps in the ethical conduct of advertising were observed and concerns were raised about the reliability of social media information and the omission of valuable details on the drug advertisements. Conclusion: Individuals have a right to informed choices that ensure their health safety. This study has highlighted the need for transparency and accountability in pharmaceutical and complementary medicine marketing on social media. Collaboration is needed between regulatory bodies, pharmaceutical companies, healthcare providers and community members, to make sure that drug advertising upholds ethical standards and public health.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".