Using Nutrition Knowledge and Diet Quality Questionnaires as Screening Tools to Identify Female Collegiate Athletes in Need of Dietitian Referral
Bibliographic record
Abstract
The purpose of this study was to evaluate nutrition knowledge and diet quality in collegiate athletes to determine if referral to a sports registered dietitian (RD) is warranted. This cross-sectional study analyzed four sections of the Nutrition for Sport Knowledge Questionnaire and the Rapid Eating Assessment for Patients Questionnaire, both validated in athletic populations. The relationship between nutrition knowledge and diet quality was evaluated. Significance was set at P ≤ 0.05. One hundred and twenty athletes reported a median nutrition score of 52 (45–61), and a dietary quality score of 53 (46–58), with a weak, positive association between both (r = 0.28 (95% CI: 0.11–0.44), P < 0.01). Fifty-four percent were categorized as needing a referral to a sports RD. Diet quality scores differed between dietitian referral group with 49 (43–54) versus 58 (52–62) for the nonreferral group, respectively (P < 0.01, V = 0.71), with no difference in nutrition knowledge observed, P = 0.73. Overall, nutrition knowledge and diet quality in our sample of collegiate athletes was poor. College athletic departments with limited access to sports RD should use these questionnaires to evaluate knowledge and the need of dietitian referral separately.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".