Dissemination of evidence in paediatric emergency medicine: a quantitative descriptive evaluation of a 16-week social media promotion
Bibliographic record
Abstract
OBJECTIVES: TRanslating Emergency Knowledge for Kids (TREKK) and Cochrane Child Health collaborate to develop knowledge products on paediatric emergency medicine topics. Via a targeted social media promotion, we aimed to increase user interaction with the TREKK and Cochrane Child Health Twitter accounts and the uptake of TREKK Bottom Line Recommendations (BLRs) and Cochrane systematic reviews (SRs). DESIGN: Quantitative descriptive evaluation. SETTING: We undertook this study and collected data via the internet. PARTICIPANTS: Our target users included online healthcare providers and health consumers. INTERVENTION: For 16 weeks, we used Twitter accounts (@TREKKca and @Cochrane_Child) and the Cochrane Child Health blog to promote 6 TREKK BLRs and 16 related Cochrane SRs. We published 1 blog post and 98 image-based tweets per week. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was user interaction with @TREKKca and @Cochrane_Child. Secondary outcomes were visits to TREKK's website and the Cochrane Child Health blog, clicks to and views of the TREKK BLRs, and Altmetric scores and downloads of Cochrane SRs. RESULTS: Followers to @TREKKca and @Cochrane_Child increased by 24% and 15%, respectively. Monthly users of TREKK's website increased by 29%. Clicks to the TREKK BLRs increased by 22%. The BLRs accrued 59% more views compared with the baseline period. The 16 blog posts accrued 28% more views compared with the 8 previous months when no new posts were published. The Altmetric scores for the Cochrane SRs increased by ≥10 points each. The mean number of full text downloads for the promotion period was higher for nine and lower for seven SRs compared with the 16-week average for the previous year (mean difference (SD), +4.0 (22.0%)). CONCLUSIONS: There was increased traffic to TREKK knowledge products and Cochrane SRs during the social media promotion. Quantitative evidence supports blogging and tweeting as dissemination strategies for evidence-based knowledge products.
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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 | 0.129 | 0.338 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".