Determinants of coaches’ intentions to provide different recommendations on sports nutrition to their athletes
Bibliographic record
Abstract
Background Coaches are considered as an important source of nutrition information by their athletes. However, their knowledge in this area is often insufficient for proper guidance and may lead to the dissemination of misinformation regarding sports nutrition. The aim of this study was to assess coaches’ intentions as well as psychosocial determinants underlying their intentions to provide sports nutrition recommendations to their high school athletes.Methods Coaches (n = 47) completed a Web-based questionnaire based on the theory of planned behaviour, to assess their intentions to provide three different sports nutrition recommendations and their determinants. Multiple regression analyses were used to identify the variables that were most strongly related to the intentions.Results Forty-six, 44.7 and 91.9% of coaches had the intention to recommend a higher consumption of foods rich in carbohydrates, foods rich in proteins and an increase in hydration to their athletes, respectively. Subjective norm was the only significant determinant of coaches’ intention to recommend a higher consumption of foods rich in carbohydrates (R 2 = 53.7%, β = 0.73 ± 0.12, P < 0.0001). Subjective norm and perceived behavioural control were significantly associated with coaches’ intentions to recommend a higher consumption of foods rich in proteins (R 2 = 25.9%, β = 0.50 ± 0.16, P = 0.002 and R 2 = 17.2%, β = 0.39 ± 0.17, P = 0.01, respectively) and an increase in hydration (R 2 = 26.8%, β = 0.38 ± 0.13, P = 0.001 and R 2 = 46.3%, β = 0.58 ± 0.11, P < 0.0001, respectively).Conclusions The results of this study suggest that subjective norm and perceived behavioural control represent important determinants of coaches’ intentions to provide recommendations on sports nutrition. These findings should be considered in future interventions aimed at facilitating proper general sports nutrition recommendations provided by coaches to their athletes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".