The role of anxiety in the association between nutrition literacy and health-related quality of life among college students
Bibliographic record
Abstract
The health-related quality of life (HRQOL) of college students is not promising and needs to be improved through effective interventions. This study aimed to investigate the associations of nutrition literacy (NL) with HRQOL and anxiety among college students, and to identify the role of anxiety in the relationship between NL and HRQOL. The cross-sectional survey was conducted via the "Wenjuanxing" platform from September to November 2023. The Food and Nutrition Literacy Questionnaire (FNLQ), the Generalized Anxiety Disorder (GAD-7) diagnostic tool and the SF-12 scale were used to assess NL, anxiety, and HRQOL, respectively. Logistic regression models, mediation analysis, additive and multiplicative interaction analyses were used. 2066 college students participated in the analysis. After adjusting for potential confounders, we found that college students with higher NL had higher HRQOL (OR = 2.52, 95% CI: 2.09-3.03, p < 0.001) and a lower risk of anxiety (OR = 0.53, 95% CI: 0.39-0.73, p < 0.001). Additionally, anxious college students had lower HRQOL (OR = 0.16, 95% CI: 0.11-0.24, p < 0.001). Furthermore, mediation analysis confirmed that anxiety was a partial mediator of the relationship between NL and HRQOL (β = 0.600, 95% CI: 0.406-0.779/0.430-0.818). Significant additive interactions were found between NL and Anxiety (RERI = 6.96, 95% CI: 2.74-11.17; AP = 0.51, 95% CI: 0.37-0.64; SI = 2.21, 95% CI: 1.58-3.07). Higher levels of NL are associated with better HRQOL and lower anxiety among college students. Additionally, anxiety partially mediated the relationship between NL and HRQOL. Furthermore, there is a synergy between NL and anxiety.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".