Development and Validation of the Military Eating Behavior Survey
Bibliographic record
Abstract
OBJECTIVE: To describe the Military Eating Behavior Survey (MEBS), developed, and validated for use in military populations. DESIGN: Questionnaire development using a 6-phase approach that included item generation, subject matter expert review, cognitive interviewing, factor analysis, test-retest reliability testing, and parallel forms testing. SETTING: US Army soldiers were surveyed at 8 military bases from 2016 to 2019 (n = 1,561). MAIN OUTCOME MEASURE: Content, face, and construct validity and reliability of the MEBS. ANALYSIS: Item variability, internal consistency, and exploratory factor analysis using principal coordinates analysis, orthogonal varimax rotation, and scree test (correlation coefficient and Cronbach alpha), as well as consistency and agreement (intraclass correlation coefficient) of test-retest reliability and parallel forms reliability. RESULTS: Over 6 phases of testing, a comprehensive tool to examine military eating habits and mediators of eating behavior was developed. Questionnaire length was reduced from 277 items to 133 items (43 eating habits; 90 mediating behaviors). Factor analysis identified 14 eating habit scales (hunger, satiety, food craving, meal pattern, restraint, diet rigidity, emotional eating, fast/slow eating rate, environmental triggers, situational eating, supplement use, and food choice) and 8 mediating factor scales (body composition strategy, perceived stress, food access, sleep habits, military fitness, physical activity, military body image, and nutrition knowledge). CONCLUSIONS AND IMPLICATIONS: The MEBS provides a new approach for assessing eating behavior in military personnel and may be used to inform and evaluate health promotion interventions related to weight management, performance optimization, and military readiness and resiliency.
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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 | 0.032 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".