Making the best food choices before competing: Developing and evaluating a nutritional intervention with the intervention-mapping framework
Bibliographic record
Abstract
This article describes the development, implementation, and evaluation of a nutritional intervention held during the Canada Summer Games – Sherbrooke, 2013. This multicomponent intervention combined environmental and educational strategies, focusing on helping athletes in making adequate food choices before their competition. Intervention mapping was used to guide the intervention process focused on food offerings, coaches, and athletes. A committee was formed, a food policy was adopted, information was sent to the coaches prior to their arrival on site, and the cafeteria was plastered with posters indicating to athletes the best food choices before competing. A convenience sample of 48 athletes and 77 coaches answered questions pertaining to how they perceived the intervention. According to the results, many of the athletes stated that the information on the posters was useful in choosing their foods. Eight of them indicated that they used the poster information to achieve better performance. Thirty-six athletes expressed an interest in seeing the intervention components displayed at future competitions and stated that they would use the poster content at other sports events. Twenty- eight athletes suggested that plans should be made to better promote the nutritional intervention before the games. Although 51 coaches stated that they paid particular attention to the nutrition of their athletes, the median outcome on knowledge questions was 63.6%. Ninety percent of the coaches indicated that they deemed the information as being relevant. Overall, this novel intervention revealed a high satisfaction level among athletes and coaches in spite of the few gaps in promotion. Key words: Diet, athlete, physical activity, food habits, food services, health promotion.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".