Sport nutrition knowledge, dietary intake and quality of life status of curling athlete populations
Bibliographic record
Abstract
Dietary Intake can impact sport performance and may be influenced by Nutrition Knowledge (NK). Stressors related to competitive sport also affect Dietary Intake and athlete Quality of Life (QOL). This is the first study to explore NK, Dietary Intake and QOL of curlers. Part 1 of the study assessed the NK (n=320) and QOL (n=240) of Canadian curlers using an author-derived questionnaire for NK and the WHOQOL-BREF and Athlete Life Quality Scale for QOL. Part 2 of the study explored the dietary intake of elite female curlers (n=4) using a 3-day food record. Data were analyzed using Mann-Whitney U, Spearman’s Correlation, Kruskal-Wallis, and Chi-Square tests. Overall, curlers had an average NK score of 69.7%, which is relatively high compared to the literature range of 33.2% - 83.7%. Nutrition Requirements, Weight Management and Macronutrients were poorly understood NK categories with Hydration, Protein and Sugar as the most well understood categories. Competitive athletes had significantly lower Total NK and General NK, but not Sport NK scores compared to recreational athletes (p=0.046, p=0.001 and p=0.449, respectively). Participants had high QOL scores with an average of 89.3%, and no differences were found between competitive and recreational athletes (p>0.05). Factors influencing QOL were Employment, as Retired participants had higher QOL scores in the Social Relationships, Psychological and Environment Domains; Gender, as male participants had higher Psychological Domain scores than female participants; and Age, that had a significantly positive relationship with the Psychological Domain (rho=0.165, p=0.011) and Environment Domain (rho=0.270, p<0.001). Dietary Intake data showed trends of low energy and carbohydrate intake, while protein and fat intake were within, and above recommendations, respectively. While this thesis concluded that Canadian curlers had high QOL scores and relatively high NK, it remains unknown how NK influences Dietary Intake, thus requiring further research in this area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".