Evaluating a Social Media Campaign for a Parent Educational Video on Bronchiolitis
Bibliographic record
Abstract
Bronchiolitis, or lower airway swelling, is a common cause of pediatric hospital admissions. Parents have expressed wishes for more information regarding bronchiolitis but had difficulty finding reliable information, suggesting the need for more effective and easily accessible information resources. Knowledge translation (KT) tools like videos provide research-based information and may be conveniently disseminated to large audiences through social media. The purpose of this project was to evaluate the effectiveness of a social media campaign to promote a video on bronchiolitis. A social media campaign was conducted from 14 October to 30 November 2019. User interactions were recorded for the Facebook and Twitter accounts, website, and YouTube of Evidence in Child Health to Enhance Outcomes (ECHO), Alberta Research Centre for Health Evidence (ARCHE), and Translating Emergency Knowledge for Kids (TREKK). Baseline metrics were collected from 1 August to 30 September 2019 and post-campaign metrics were collected from 1 December 2019 to 31 March 2020. Mean monthly changes, standard deviations, and percent changes between periods were generated for the baseline, campaign, and post-campaign periods. Overall, there was a visible increase in user interactions throughout the campaign period. There was an overall downward trend in user interactions following the campaign. These findings suggest that social media may be a useful method of KT tool dissemination when consistently used. The downward trend post-campaign highlights the need for further research to investigate methods to maintain continuous interaction following a campaign.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".