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Record W2899657911 · doi:10.5539/gjhs.v10n12p21

Nutritional Behaviors and Perceived Barriers Among University Students: A Cross-Sectional Study

2018· article· en· W2899657911 on OpenAlexvenueno aff
Abdul‐Monim Batiha

Bibliographic record

VenueGlobal Journal of Health Science · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyHealthy eatingEnvironmental healthMedicineFish <Actinopterygii>GerontologyHealthy foodPsychologyFood sciencePhysical activityPhysical therapy

Abstract

fetched live from OpenAlex

PURPOSE: This study was a) performed to evaluate to what degree Jordanian university students display healthy nutritional behavior, b) which socio-demographic variables impact on it, and c) to identify perceived barriers toward not eating healthy foods. METHOD: A cross-sectional descriptive survey based on a questionnaire, was completed by Jordanian university students (n =1.180). RESULTS: Students show a low level of a nutritional behaviors score (10 from 20).The most common types of food/drink consumed were caffeine drinks and the rarely or never consumed one was fish. The perceived barriers for not eating healthy food reported were: i) Healthy foods are expensive, ii) Lack of time, iii) Poor choice of healthy foods, iv) living alone at home, v) Don&amp;rsquo;t know how to cook, and vi) Don&amp;rsquo;t like/enjoy healthy foods. CONCLUSIONS: Increased nutritional awareness and healthier nutrition are needed to improve nutritional behavior because it connected with improving health status and academic achievement. Perceived barriers for not eating healthy food should be considered in planning university nutritional programs.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.467
Teacher spread0.418 · 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.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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