Public health perinatal promotion during COVID-19 pandemic: a social media analysis
Bibliographic record
Abstract
BACKGROUND: Canadian public health agencies, both municipal/regional and provincial/territorial, are responsible for promoting population health during pregnancy and the early postnatal period. This study examines how these agencies use web-based and Facebook channels to communicate perinatal health promotion during the emergence of the COVID-19 pandemic. METHODS: Perinatal health promotion content of websites and Facebook posts from a multijurisdictional and geographically diverse sample of government and non-governmental organizations (NGO) were evaluated using thematic content analysis in 2020. RESULTS: Major Facebook perinatal health promotion themes included breastfeeding, infant care, labor/delivery, parenting support and healthy pregnancy. Facebook COVID-19-themed perinatal health promotion peaked in the second quarter of 2020. Websites emphasized COVID-19 transmission routes, disease severity and need for infection control during pregnancy/infant care, whereas Facebook posts focussed on changes to local health services including visitor restrictions. NGO perinatal health promotion reflected organizations' individual mandates. CONCLUSIONS: Canadian government use of Facebook to disseminate perinatal health promotion during the COVID-19 pandemic varied in terms of breadth of topics and frequency of posts. There were missed opportunities to nuance transmission/severity risks during pregnancy, thereby proactively countering the spread of misinformation.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".