Evaluating the Impact of an Evidence-Based Social Media Campaign Among Elementary Educators
Bibliographic record
Abstract
The SHS LEARN Lab is an open-access, evidence-based repository of health resources developed to support elementary teachers following educational disruptions during the pandemic. The purpose of this study was to explore the feasibility of using social media as a promotional method for the health resource repository across four social media platforms. Using platform-specific insight tools, metrics were collected from each account during two consecutive campaign periods between November to December 2023. TikTok content was viewed by the most individuals (n = 6,100), followed by YouTube (n = 2,403), and Instagram (n =190). Across all platforms, weekly activity predominately met “average” to “above average” engagement rate thresholds. Analyses revealed a significant difference in engagement rates between X and YouTube from Weeks 1 to 8 (t(14) = 2.27, p < 0.05). This study provides a framework to analyze social media performance and underscores the campaigns effectiveness on health resource accessibility among teachers.
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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 | 0.028 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".