A Potential Screening Tool for Nutritional Preparedness in Collegiate Level Female Athletes: A Pilot study
Bibliographic record
Abstract
Background: Diet monitoring is part of an athlete’s health and performance assessment, and adequate nutrition is known to be a method that can positively influence the reduction in exercise-induced injury. However, the concept of nutritional preparedness as a screening tool to identify low energy availability for the competitive season is not mainstream practise. Objectives: Our pilot study investigated three aims: 1) changes to nutritional status from the pre-competition phase to the competition phase, 2) living status impact on athlete’s food accessibility, and 3) whether nutritional preparedness in the pre-competition phase influenced the potential for low energy availability during the competition phase. Methods: Female volleyball athletes (N=21, 19-22 yrs., 80% lived off campus) were recruited from 3 universities (Ambrose, Calgary, New Brunswick- Saint John) through social media sites, and word of mouth. Two cross-sectional questionnaires (questions derived from the Short Food Frequency-Q, LEAF-Q, and RED-S screening tool-Q) were administered prior to and during the competitive season. Results: The nutritional assessment score significantly decreased from the pre-competition to competition phase, respectively (n=20, 26.11 ± 4.25; n=12, 20.64 ± 4.74; p=0.022). Many athletes (6/12) reported an injury during the competitive season with an average time loss from sport of 8-14 days. Conclusions: These findings suggest that collegiate female volleyball athletes have a potential for low energy availability, regardless of living status. Future research should build on the nutritional preparedness concept as a method of screening for low energy availability and the influence on injuries sustained during the competition phase.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".