Exploring Influences of Eating Behaviors Among Emerging Adults in the Military
Bibliographic record
Abstract
OBJECTIVE: Identify factors influencing eating behaviors among emerging adults in the military. DESIGN: Focused ethnography using interviews, observations, and artifacts for data. SETTING: Three US Naval installations. PARTICIPANTS: Thirty-two active-duty Sailors aged 18-25 years. ANALYSIS: Qualitative data were organized in NVivo and analyzed sequentially to categorize culturally relevant domains and themes using a social ecological model (SEM). Descriptive statistics were used to describe questionnaire data in SPSS (version 27.0, IBM, 2020). RESULTS: Leaders encouraged healthy eating through policies and messages, but cultural contradictions and environmental barriers undermined Sailors' efforts to eat healthily. Stress and resource constraints (intrapersonal), peer pressure (social), unhealthy food environments and lack of access to food preparation (environmental), and eating on the go because of mission-first norms (cultural) promoted unhealthy eating behaviors. Nutrition and culinary literacy (intrapersonal); peer support and leadership engagement (social); access to healthy, convenient, and low-cost foods (environmental); and indoctrination to healthy eating during recruit training (cultural) positively influenced eating behaviors. CONCLUSION AND IMPLICATIONS: The eating behaviors of service members are influenced by many modifiable factors. Targeted education, leadership engagement, and policies that make nutritious foods easily accessible, appealing, and preferred are needed.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".