A Proposed Conceptual Sport Nutrition Approach for Athlete Development and Assessment: The Athlete Nutrition Development Approach
Bibliographic record
Abstract
Appropriate dietary intake can improve athletes' health and sport performance and is a direct result of eating behaviours. Therefore, assessing and shaping athletes' eating behaviours and dietary intake is critical to the provision of sport nutrition services. As such, nutrition practitioners must also consider the determinants of eating behaviours. However, dietary intake, eating behaviours, and its determinants are inconsistently defined in the literature, requiring nutrition practitioners to navigate a complicated landscape of concepts and terminology. This is further complicated by limitations in practically measuring and influencing eating behaviours and dietary intake. The proposed Athlete Nutrition Development Approach was developed to aid practitioners in servicing decisions through the athlete development process, through a three-tiered approach to sport nutrition service delivery. Tier 1 addresses the determinants of eating behaviours, Tier 2 directly addresses eating behaviours and dietary intake, and Tier 3 addresses the consequences of dietary intake in relation to health and sport performance. Each tier includes tools for assessment and development.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".