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Record W3107316433 · doi:10.4103/ijpvm.ijpvm_138_19

Patterning of Food Preferences Among Iranian Adults: Results from SEPAHAN Study

2020· article· en· W3107316433 on OpenAlexaff
Maedeh Moradi, Ammar Hassanzadeh Keshteli, Awat Feizi, Leila Azadbakht, Ahmad Esmaillzadeh, Peyman Adibi

Bibliographic record

VenueInternational Journal of Preventive Medicine · 2020
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFood preferenceMedicinePreferenceFood frequency questionnaireFood choiceWine tastingEnvironmental healthPopulationFood groupFood scienceBiology

Abstract

fetched live from OpenAlex

Background: The present study was conducted to evaluate the pattern of food preference among a large sample of Iranian adults. Methods: In a cross-sectional study within the study on the epidemiology of psychological alimentary health and nutrition (SEPAHAN) projects, a total of 6239 of 8694 subjects completed a 106-item food preference questionnaire. Subjects indicated whether they liked, disliked or had gastrointestinal symptoms for each food item separately. They also reported the frequency of consumption for each food item. Results: We observed that presence of some foods such as yogurt, fruits and vegetables in the list of the most preferred food items and presence of kalbas, sausages and chips in the list of the most disliked food items, were representative of healthy dietary pattern in this population. Results also revealed that women liked unhealthy foods more than men ( P value <0.05 for all significant food items). Moreover, in most of the food items, men reported less gastrointestinal symptoms than women ( P value <0.05 for all significant food items). Our findings revealed that smokers disliked most of the healthy food items. We also observed that pregnant women regardless of the trimesters, reported dislike for sweet-tasting food items. Conclusions: More researches are suggested in order to indicate the origins of preferences and recommend some practical alternatives to improve the dietary pattern in society.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.349
Teacher spread0.302 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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