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Record W4285094617 · doi:10.1007/s10903-022-01373-1

Investigating Asian American Adolescents’ Resiliency Factors and Young Adult Mental Health Outcomes at 14-year Follow-up: A Nationally Representative Prospective Cohort Study

2022· article· en· W4285094617 on OpenAlexaff
Puja Iyer, Deepika Parmar, Kyle T. Ganson, Jennifer Tabler, Samira Soleimanpour, Jason M. Nagata

Bibliographic record

VenueJournal of Immigrant and Minority Health · 2022
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood InstituteAmerican Heart Association
KeywordsMental healthOdds ratioPublic healthLogistic regressionLongitudinal studyProspective cohort studyMedicineCohort studyGerontologyPsychologyOddsYoung adultConfidence intervalDemographyPsychiatry

Abstract

fetched live from OpenAlex

There is scant research on how Asian American adolescents' resiliency relates to mental well-being in adulthood. The objective of this study was to determine the prospective associations between resiliency factors (individual, family, and school community) in adolescence and mental health outcomes in adulthood, among a national sample of Asian Americans. We analyzed data from 1020 Asian American adolescents who were followed for 14 years in the National Longitudinal Study of Adolescent to Adult Health. Of the resiliency factors, individual self-esteem (Adjusted Odds Ratio [AOR] 0.54, 95% Confidence Interval [CI] 0.37-0.79) and family connectedness (AOR 0.78, 95% CI 0.65-0.93) in adolescence were found to be protective against adult mental health outcomes in logistic regression models adjusting for sociodemographic factors and baseline mental health. Our study identified individual and family resiliency factors which can be leveraged to help Asian American adolescents and families in cultivating better mental health.

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)0.001
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.360
Teacher spread0.342 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

Explore more

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