Mapping of Health Communication and Education Strategies Addressing the Public Health Dangers of Illicit Online Pharmacies
Bibliographic record
Abstract
Illicit online pharmacies are a growing global public health concern. Stakeholders have started to engage in health promotion activities to educate the public, yet their scope and impact has not been examined. We wished to identify health promotion activities focused on consumer awareness regarding the risks of illicit online pharmacies. Organizations engaged on the issue were first identified using a set of engagement criteria. We then reviewed these organizations for health promotion programs, educational components, public service announcements, and social media engagement. Our review identified 13 organizations across a wide spectrum of stakeholders. Of these organizations, 69.2% (n = 9) had at least one type of health promotion activity targeting consumers. Although the vast majority of these organizations were active on Facebook or Twitter, many did not have dedicated content regarding online pharmacies (Facebook: 45.5%, Twitter: 58.3%). An online survey administered to 6 respondents employed by organizations identified in this study found that all organizations had dedicated programs on the issue, but only half had media planning strategies in place to measure the effectiveness of their programs. Overall, our results indicate that though some organizations are actively engaged on the issue, communication and education initiatives have had questionable effectiveness in reaching the public. We note that only a few organizations offered comprehensive and dedicated content to raise awareness on the issue and were effective in social media communications. In response, more robust collaborative efforts between stakeholders are needed to educate and protect the consumer about this public health and patient safety danger.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".