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Record W4375862030 · doi:10.1016/j.jneb.2023.02.004

Exploring Influences of Eating Behaviors Among Emerging Adults in the Military

2023· article· en· W4375862030 on OpenAlexvenueno aff
Melissa R. Troncoso, Candy Wilson, Jonathan M. Scott, Patricia A. Deuster

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
FundersUniformed Services University of the Health SciencesGovernment of South AustraliaU.S. Department of Defense
KeywordsIntrapersonal communicationPsychologySocial psychologyApplied psychologyInterpersonal communication

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.135
GPT teacher head0.462
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2023
Admission routes1
Has abstractyes

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