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Record W7133070682

Depression, Emotional Eating, and Cardiovascular Disease Risk in Children and Adolescents

2024· dissertation· W7133070682 on OpenAlexfundno aff
Jessica Muha

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsDepression (economics)Association (psychology)Context (archaeology)DiseasePsychological interventionEmotional eatingDepressive symptomsMental health
DOInot available

Abstract

fetched live from OpenAlex

Depression, a burdensome mental health disorder, increases risk of early cardiovascular disease (CVD) in youth. Although the mechanism of association is unknown, emotional eating, an eating behaviour corresponding to eating in response to emotions, may contribute as it relates to both depression and obesity. However, this is poorly understood within the context of children and adolescents. This dissertation explores the role of emotional eating in the association between depression and CVD risk in children and adolescents. Based on meta-analysis of the current literature, depressive symptoms and emotional eating are associated in youth. Further analysis of an outpatient psychiatry program reveals the depression-CVD association is present early in the course of illness and emotional eating is associated with increased CVD risk among female youth with depression. These findings offer better understanding of emotional eating in the depression-CVD risk association and provide initial support for eating-targeted preventative interventions among youth with depression.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.310
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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