The Development, Implementation, and Evaluation of an Education-Based Health Promotion Social Media Campaign Targeting Elementary Educators
Bibliographic record
Abstract
Elementary teachers are currently facing the untenable position of needing to do more with less following the educational disruptions of the COVID-19 pandemic. The School of Health Studies (SHS) LEARN Lab is an open-access, evidence-based health resource repository designed to support teachers who are experiencing educational gaps among students in their classrooms. One strategy to increase the uptake of resources is through a health promotion social media campaign, specifically on Instagram and TikTok—two of the largest social media platforms used by teachers to seek out educational resources. As such, the purpose of this study was to develop, implement, and evaluate the effectiveness, based on key performance indicators (i.e., reach and engagement), of an 8-week health promotion social media campaign to increase engagement with the SHS LEARN Lab’s open access resource repository. Overall, the campaign was successful in increasing uptake of the LEARN Lab’s resources. Reach and engagement rates across platforms were above average, and a statistically significant difference between the reach rate across platforms was found during the campaign ( t (14) = 6.189, p < .001). Further, there was a statistically significant increase in average engagement rate on Instagram from baseline to during campaign ( t (7) = 6.871, p < .001). This study offers a template for future campaigns to follow when developing, implementing, and evaluating health promotion social media campaigns. Health promoters and decision-makers in the educational sector should consider social media as a cost-effective and feasible mechanism to increase teacher-specific supports.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".