Dissemination Using Infographic Reports Depicting Program Impact of a Community-Based Research Program: eB4CAST in iCook 4-H
Bibliographic record
Abstract
OBJECTIVE: To evaluate barriers to dissemination and implementation and perceptions of the Evidence-Based Forecast Capture, Assemble, Sustain, Timelessness (eB4CAST) dissemination infographic tool as part of iCook 4-H dissemination. DESIGN: Online surveys and phone interviews. PARTICIPANTS: Experts (n = 35) in community research completed the survey; 13 completed the interview. MAIN OUTCOMES MEASURE: Experts' perceptions of eB4CAST reports used for iCook 4-H dissemination. ANALYSIS: Frequency and thematic analysis. RESULTS: Survey respondents agreed (85%) that the eB4CAST infographic provided a clear understanding of iCook 4-H and relevant information (83%). Statistics included in the infographic were reported as easily understood (66%). Respondents (83%) stated that the infographic would be helpful to share outcomes with stakeholders. Thematic analysis showed that the majority of interviewees agreed that eB4CAST infographics might aid in overcoming barriers to dissemination and implementation including communication and community ownership. CONCLUSIONS AND IMPLICATIONS: This study provides perceptions from experts regarding the value of using eB4CAST infographics as a tool to disseminate the impact of a community nutrition program.
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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 | 0.040 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".