Psychological and Social Factors Influencing Eating Behaviors in College Athletes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it