Psychological and Social Factors Influencing Eating Behaviors in College Athletes
Bibliographic record
Abstract
Eating behaviors among college athletes are influenced by a complex interplay of psychological, social, and nutritional factors, which can significantly impact their health and performance. This study aims to elucidate these multifaceted influences, providing a deeper understanding of the factors that shape eating behaviors in collegiate sports environments. This qualitative study involved semi-structured interviews with 30 collegiate athletes from various sports disciplines across several universities. Theoretical saturation guided the data collection process until no new themes emerged. Data were transcribed verbatim and analyzed using NVivo software to conduct thematic analysis, focusing on identifying patterns related to psychological drivers, social influences, and nutritional knowledge. Three main themes were identified: Psychological Drivers, Social Influences, and Nutritional Knowledge. Psychological Drivers included Emotional Eating, Dietary Attitudes, and Motivation to Eat Well. Social Influences encompassed Peer Dynamics, Family Influence, Coaching Guidance, and Social Media Impact. Nutritional Knowledge was characterized by Understanding of Nutrition, Sources of Information, and Dietary Planning. Each theme and its categories were supported by specific concepts illustrating the complex and interconnected factors influencing athletes' eating behaviors. The study highlighted the significant role of psychological and social factors alongside nutritional knowledge in shaping the eating behaviors of college athletes. Interventions aimed at improving athletes' eating behaviors should consider these dimensions to effectively support athletes in managing their dietary habits in a way that promotes both optimal performance and general well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".