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Record W3137616530 · doi:10.1521/bumc.2021.85.1.42

Prevalence, severity, and clinical correlates of food neophobia in Salvadorian youth

2021· article· en· W3137616530 on OpenAlexaff
Robert R. Selles, Monica S. Wu, Juan Carlos Novoa, Raquel Marina Zepeda-Burgos, Daniel Guttfreund, Nicole M. McBride, Dean McKay, Eric A. Storch

Bibliographic record

VenueBulletin of the Menninger Clinic · 2021
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsNeophobiaAnxietyPsychologyClinical psychologyPopulationPsychiatryMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Food neophobia (FN) describes problematic fear-based avoidance/restriction of novel foods. Using the novel parent-reported Measure of Food Neophobia (MFN), the authors examined FN symptoms and impairment in 305 Salvadorian children and explored clinical correlates. Factor analysis supported the MFN's designed structure to inquire about FN symptoms and FN impairment. At least one FN symptom was observed in 91% of the sample; however, only 9% were rated as having moderate or greater impairment. Demographic variables were not associated with FN; however, FN was positively correlated with internalizing, externalizing, inattention, health anxiety, and obsessive-compulsive symptoms. Inattention symptoms and health anxiety emerged as significant predictors of FN symptoms, while FN symptoms were the only significant predictor of FN impairment. The present study supports the MFN and provides information about FN in a general population, including the prevalence of clinically significant symptoms and association with psychological domains. Continued investigation of FN is still 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 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 score0.002
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.329
Teacher spread0.290 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

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