Food and Mood: Daily Associations Between Missed Meals and Affect Among Early Adolescents
Bibliographic record
Abstract
Objective Youth frequently miss meals and experience hunger, yet no studies have assessed how these experiences relate to changes in daily emotional states. This daily diary study examined associations of missing breakfast, missing lunch, and hunger with daily positive and negative affect among young adolescents.Methods A community sample of 133 grade 5 and 6 students (50.4% boys, Mage = 10.77, 19.5% BIPOC) from two public schools completed baseline socio-demographic measures at the beginning of the study and daily measures at the end of the school day over 5 consecutive days. Measures included positive and negative affect, breakfast, lunch, and hunger. Multilevel regression models were constructed to test the associations.Results Among participating youth, 27.8% missed breakfast at least once, 15.0% missed lunch at least once, and 26.3% felt hungry at least once. Missing breakfast was associated with increased negative affect (B = 0.36, p = .030) and missing lunch was related to both increased negative affect (B = 0.52, p = .019) and decreased positive affect (B = −0.80, p = .002). Hunger was not related to daily affect.Conclusion This study provides a unique view of youths’ experiences of missing meals, hunger, and daily emotional states. The findings underscore the importance of youth being adequately nourished through school meal programs. Clinicians should screen for and address missing meals among their young patients.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".