MétaCan
Menu
Back to cohort
Record W4293916752 · doi:10.1097/nmd.0000000000001584

Inhibition Predicts the Course of Depression and Anxiety Symptoms Among Adolescents

2022· article· en· W4293916752 on OpenAlexaff
Elizabeth S. Stevens, Carter J. Funkhouser, Randy P. Auerbach, Ardesheer Talati, Marc G. Gameroff, Jonathan Posner, Myrna M. Weissman, Stewart A. Shankman

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2022
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsColumbia College
FundersNational Institute of Mental Health
KeywordsAnxietyDepression (economics)PsychologyDepressive symptomsClinical psychologyLongitudinal studyPsychiatryMedicine

Abstract

fetched live from OpenAlex

ABSTRACT: Numerous theoretical models suggest that inhibition difficulties-the inability to moderate automatic responses-contribute to the onset and/or maintenance of internalizing symptoms. Inhibition deficits and internalizing disorders run in families and share overlapping genetic risk factors, suggesting that inhibition deficits may be particularly prognostic of internalizing symptoms in those with high familial risk. This study tested this hypothesis in a longitudinal sample during the transition from adolescence to early adulthood. As hypothesized, prospective associations between inhibition and anxiety and depressive symptoms 8 years later were moderated by familial risk for depression. Specifically, poorer inhibition prospectively predicted greater anxiety and depressive symptoms in those at high (but not low) familial risk for major depressive disorder. These findings provide preliminary support for impaired inhibition as an indicator of risk for later internalizing symptoms in those at high familial risk.

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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Nervous and Mental DiseaseSame topicChild and Adolescent Psychosocial and Emotional DevelopmentFrench-language works237,207