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Record W4293051456 · doi:10.1002/ajcp.12624

The gendered relationship between cumulative exposure to lower community attachment and adolescent health

2022· article· en· W4293051456 on OpenAlexaff
Gum‐Ryeong Park, Jinho Kim

Bibliographic record

VenueAmerican Journal of Community Psychology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsHealth psychologyConfoundingSelf-rated healthLongitudinal studyAdolescent healthPsychologyCommunity healthPsychological interventionPublic healthAssociation (psychology)Cumulative effectsLongitudinal dataDemographyMedicineEnvironmental healthDevelopmental psychologyGerontologyPsychiatry

Abstract

fetched live from OpenAlex

This study examines whether the longitudinal association between cumulative exposure to lower community attachment and adolescent health differs by gender. Using seven waves of the Korean Children and Youth Panel Survey spanning 2010-2016, this study examines the association between cumulative exposure to lower community attachment and self-rated health among Korean adolescents. This study estimated fixed-effects models to account for unobserved confounders at the individual level. Fixed-effects estimates revealed that cumulative exposure to lower community attachment is associated with a decreased likelihood of reporting excellent health. Starting from the initial exposure, girls' self-rated health continued to deteriorate over time. In contrast, boys' self-rated health decreased for up to 3 years of persistent exposure, but has since returned to pre-exposure levels. The association between cumulative exposure to lower levels of community attachment and a decline in self-rated health is more pronounced among girls than boys. Gender-specific community-based interventions during adolescence may be required to promote adolescent health and well-being.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.166
GPT teacher head0.477
Teacher spread0.311 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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