Psychosocial Factors and the Effects of a Structured Injury Prevention Workshop on Coaches’ Self-Efficacy to Implement the 11+ Exercise Program
Bibliographic record
Abstract
Psychosocial factors have both direct and indirect influence on behavior change. Self-efficacy is a key psychosocial factor driving behavior change. It is an individual's perceived capability of performing a desired action. Structured injury prevention workshops targeting improvements in psychosocial factors in coaches may enhance the dissemination and implementation of the 11+ program in community settings. This study describes baseline psychosocial factors in youth soccer coaches and the effects of a structured 11+ injury prevention workshop on coaches' self-efficacy to implement the 11+. An adapted questionnaire based on the Health Action Process Approach Model was administered to a sample of coaches, before and after an 11+ workshop. Measures of self-efficacy included: their understanding of the 11+; their ability to use the 11+; using the 11+ with limited space, and using the 11+ when players lacked interest. Data from 73 of 81 coaches were retained for analyses. The majority (74%) of coaches knew about the 11+ program before the workshop, mostly through internet resources and colleagues. 40% to 55% of coaches had at least one unit increase (range, 1 to 6); 29% to 48% did not have a change in measures of self-efficacy. Ten percent to 24% had at least one unit decrease (range, -1 to -3). Wilcoxon matched-pairs signed-ranks test (with Bonferroni correction) indicated significant increases in coaches' post-workshop (compared to baseline) mean ranks for three of the four self-efficacy measures (p≤0.013). A structured workshop significantly improved self-efficacy towards the implementation of the 11+ program in youth soccer coaches.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".