Exploration for a knowledge translation model in public dissemination via social media: Insights from an innovative Cochrane evidence dissemination competition in China
Bibliographic record
Abstract
Objective: This study aimed to present and evaluate an innovative evidence dissemination competition based on Cochrane evidence through participants' perceptions, barriers, and feedback about evidence dissemination. Methods: We evaluated competition participation, the submissions and their dissemination on social media. An online questionnaire was also conducted to analyze participants' perceptions, barriers, and feedback about the competition and evidence dissemination. Microsoft Excel and IBM SPSS (26.0) were used to analyze the data. Results: A total of 80 text-graphics and 14 short videos submissions in 10 dissemination formats were created by 173 participants. Once disseminated on WeChat, submissions received considerable attention. Questionnaires received revealed the most chosen motivations for participation in personal skills development, including "furthering studies in EBM" (60.7%, 105/173). Previous works (64.2%, 108/173) and social media information (53.2%, 92/173) were the main sources of inspiration. Lack of knowledge in clinical trials was the most prevalent barriers. Only few participants (medical background: 12.9%, nonmedical background: 18.3%) could understand the original English version without translation. Suggested improvement for the competition included "increase competition impact," while expectations for future evidence dissemination included "disseminate through more social media platforms." Conclusions: The third Cochrane Dissemination Competition provided a valuable opportunity for participants to demonstrate their creativity while deepening their understanding of medical evidence, representing a successful attempt to disseminate high-quality Cochrane evidence to the public via Chinese social media. Key elements identified provide valuable insights for evidence dissemination to the public by health professionals via social media.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".