Understanding of and Motivations for Following Plant-Based Eating Patterns in University-Aged Competitive Athletes
Bibliographic record
Abstract
Plant-based eating (PBE), emphasizing grains, legumes, and fruits and vegetables, is linked to chronic disease prevention, positive environmental impacts, and sound animal ethics. Misconceptions of PBE and how PBE affects performance and recovery may prevent athletes from following a dietary pattern that promotes good health and environmental sustainability. The objectives of this study were to: (1) determine if competitive athletes’ definitions of PBE are consistent with those of current North American nutrition guidelines, and (2) understand if competitive athletes’ motivations to follow PBE are related to perceived impacts on performance in their sport. Twenty male and female athletes (18 years or older) competing in university sports at 4 Canadian post-secondary schools completed an online questionnaire consisting of open-ended questions on personal definitions, practices, and concerns with PBE. Responses were analyzed using grounded theory, and thematic and content analysis was used to find commonalities amongst responses. Data was analyzed by a research team consisting of an experienced qualitative researcher, a senior researcher, and a senior undergraduate student. Definitions of PBE amongst athletes varied; however, the majority viewed PBE as complete omission of meat or animal products. 16 participants indicated that they did not follow PBE. A theme of uncertainty of the effects of PBE on performance emerged from the data (“I don't know where I would get the strength to finish workouts” and “I understand that eating fruits and vegetables is important for general health, but I also think that a high protein diet, which is easily obtained by eating meat, is important as well)." A theme of openness to trying PBE was also apparent from the data, with athletes reporting that they would follow PBE if it helped them achieve their nutrition goals and induced a noticeable difference in the way they felt. Misconceptions and lack of knowledge of PBE and its effects on performance may lead athletes to overlook PBE as a viable option. Future nutrition education for athletes should promote clear definitions of PBE and how emphasizing plant-based foods can support both athletic performance and optimal health. This study was unfunded by an internal grant from The University of Winnipeg.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".