The impact of infographics on disseminating sport psychology resources to athletes
Bibliographic record
Abstract
Background: Effective knowledge dissemination is critical for ensuring that research findings reach their intended audiences and inform practice. In sport psychology, there is a growing call for improved strategies to bridge the research-to-practice gap. Infographics, which visually synthesize key concepts, are a promising tool for engaging athletes and enhancing knowledge retention. Despite their growing use across fields, their effectiveness in sport psychology remains underexplored. Purpose: This study evaluated the impact of a sport psychology infographic on athletes’ knowledge comprehension and retention. Methods: A two-part research design including a cross-sectional survey and a longitudinal follow-up was employed: (Part 1) participants assessed the infographic’s visual appeal, content quality, and persuasiveness as well as reported their intention to seek additional resources and share the infographic; (Part 2) a subset of participants completed a follow-up survey one month later to assess knowledge retention. Results: Most participants were satisfied with the infographic’s visual appearance (71.7%) and demonstrated a high level of content understanding (75.5%). A majority expressed intentions to seek additional resources (73.8%), share the infographic (65.5%), and implement the information provided (55.2%). A Friedman’s test revealed significant differences in the sequence of recalled information compared to the original infographic layout, N = 13, χ² (5) = 25.703, p = .001. Conclusions: These findings support the use of infographics as an effective knowledge translation product in sport psychology, with positive outcomes in athlete engagement, comprehension, and retention.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".