Developing an FASD Health Promotion Social Media Campaign Using Community-Based Participatory Peer Review: Notes From Atlantic Canada
Bibliographic record
Abstract
Introduction: Health promotion awareness activities by health agencies, such as those by the Public Health Agency of Canada, aim to increase health literacy, often using social media campaigns to reach large audiences. Objectives: The objective of this research was to explore how community peer-reviewed social media content can facilitate the reach of health promotion campaigns, with specific regard to awareness around fetal alcohol spectrum disorder (FASD), alcohol, and pregnancy in Atlantic Canada. Methods: We developed a health promotion social media campaign consisting of 16 digital posters, then implemented a community-based participatory peer-review process to request input and feedback from individuals with lived experiences, caregivers, professionals who work with individuals with FASD, and various advocates for FASD throughout Atlantic Canada. Reviewer comments were then directly applied to update poster content, which was redistributed for secondary approval before circulation on Facebook and Instagram. Results: Reviewer feedback was constructive, with various suggestions for updates being applied to images and text choices. Once posted, the health promotion campaign was well received and reached over half a million viewers across the two platforms in the first three months. Engagement and interactions were overall positive, with minimal negative responses and only one post flagged by the platform as a social issue advertisement. Discussion/Conclusion: With the inclusion of diverse voices from the FASD community in the co-creation of the content, we argue that the campaign’s style, messaging, and language more accurately reflected the intended audiences and was made accessible to a wider demographic, thus strengthening awareness and prevention messages. We explore a health promotion social media campaign development process, providing valuable examples of how to encourage and create more safe spaces that promote open conversations about health and gradually dismantle outdated ideas and systems so we can begin addressing stigma by delivering accurate, non-judgmental health information.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".