Are public health researchers designing for dissemination? Findings from a national survey in China
Bibliographic record
Abstract
Abstract Background Research findings are not always disseminated in ways preferred by audiences, and research dissemination is not always considered a priority by researchers. While designing for dissemination (D4D) provides an active process to facilitate effective dissemination, use of these practices in China is largely unknown. We aimed to describe the designing for dissemination activities and practices among public health researchers in China. Methods In January 2022, we conducted a cross-sectional survey in 61 sub-committees of four national academic societies which include a wide range of health disciplines. The sample mainly involved researchers at universities or research institutions, the Centers for Disease Control and Prevention at national or regional levels, and hospitals. Participants completed a 42-item online questionnaire. Respondent characteristics, dissemination routes, dissemination barriers, organizational support, and personal practice of D4D were examined with descriptive analyses. Results Of 956 respondents, 737 were researchers. Among these researchers, 58.1% had disseminated their research findings. Although there were some variation in the commonly used routes among different groups, academic journals (82.2%) and academic conferences (73.4%) were the most frequently used routes. Barriers to dissemination to non-research audiences existed at both organizational level (e.g., a lack of financial resources, platforms, and collaboration mechanisms) and individual level (e.g., a lack of time, knowledge, and skills, and uncertainty on how to disseminate). About a quarter of respondents (26.7%) had a dedicated person or team for dissemination in their unit or organization, with university researchers reporting a significantly higher proportion than their counterparts (P < 0.05). Only 14.2% of respondents always or usually used frameworks or theories to plan dissemination activities, 26.2% planned dissemination activities early, and 27.1% always or usually involved stakeholders in the research and dissemination process. Respondents with working experience in a practice or policy setting or dissemination and implementation training experience were more likely to apply these D4D strategies (P < 0.05). Conclusion Considerable room exists for improvement in using impactful dissemination routes, tackling multiple barriers, providing organizational support, and applying D4D strategies among Chinese public health researchers. Our findings have implications for structural changes in academic incentive systems, collaborations and partnerships, funding priorities, and training opportunities.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".