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Record W3045751189 · doi:10.1177/2167696820943028

Diet and Mental Health During Emerging Adulthood: A Systematic Review

2020· review· en· W3045751189 on OpenAlexaff
Sam Collins, Sarah Dash, Steven Allender, Felice N. Jacka, Erin Hoare

Bibliographic record

VenueEmerging Adulthood · 2020
Typereview
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMental healthAnxietyClinical psychologyDepression (economics)PsychologySuicidal ideationSystematic reviewAffect (linguistics)Meta-analysisGrading (engineering)PsychiatryMedicineMEDLINESuicide preventionPoison controlEnvironmental health

Abstract

fetched live from OpenAlex

Previous research has reported associations between diet and risk of depression and anxiety; however, this is underexplored in emerging adulthood (EA; 18–29 years). This systematic review examined associations between diet quality and common mental disorders and their related symptomatology in the published EA literature. A systematic search according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted for articles published between 2009 and 2019. Grading of evidence was performed using an established quality assessment tool for quantitative studies. Sixteen studies were included for review. Findings supported EA as a risk period for both poor mental health and low diet quality. There was moderate support for associations between diet quality and depression, anxiety, positive/negative affect, suicide ideation, and psychological health. Methodological quality overall was weak. EA appears to be a critical period for both diet quality and mental health. Further research is needed to better understand diet and mental health associations among EAs.

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)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.336
Teacher spread0.312 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations59
Published2020
Admission routes1
Has abstractyes

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