Additional quantitative survey data.
Bibliographic record
Abstract
<div> Social media platforms, such as Instagram, are increasingly used as a source of health information; however, it is unclear how to effectively leverage these platforms during public health emergencies. @PandemicPregnancyGuide (PPG) was an Instagram account created by Canadian physicians to provide perinatal health information during the COVID-19 pandemic. We conducted a cross-sectional survey, and assessed Instagram analytics, to determine how and why users followed PPG and its impact on health decision-making. Respondents most valued posts explaining scientific articles in lay language and the delivery of content by medical experts. Topics of greatest interest were COVID-19 vaccination while pregnant (76%), COVID-19 infection during pregnancy (71%), and labour and delivery during the pandemic (69%). Respondents self-reported being more likely to use COVID-19 protective measures while pregnant (80%), receive COVID-19 vaccines in pregnancy (87%), and vaccinate their children against COVID-19 (58%) due to the information shared by PPG. Taken together, we demonstrate how healthcare professionals can effectively leverage social media to disseminate health information and improve uptake of public health recommendations. We recommend consideration of our findings in the development of future health-based social media platforms, particularly during public health emergencies or campaigns. </div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.985 | 0.031 |
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; both teacher heads 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".