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Record W1994878987 · doi:10.1037/0278-6133.25.6.704

Neighborhood, family, and subjective socioeconomic status: How do they relate to adolescent health?

2006· article· en· W1994878987 on OpenAlexafffund
Edith Chen, Laurel Q. P. Paterson

Bibliographic record

VenueHealth Psychology · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsSocioeconomic statusHostilityPsychologyBody mass indexPsychological interventionDevelopmental psychologyMental healthClinical psychologyMedicineEnvironmental healthPsychiatryPopulation

Abstract

fetched live from OpenAlex

This study investigated the role of neighborhood, family, and individual subjective socioeconomic status (SES) in predicting adolescent physical health and psychological characteristics. Three hundred fifteen adolescents completed assessments of blood pressure, cortisol, and body mass index (BMI). Results revealed that lower neighborhood SES was associated with higher BMI and lower basal cortisol levels and that these effects persisted after controlling for family SES. Both family SES and neighborhood SES predicted negative psychological characteristics and experiences such as hostility and discrimination. In contrast, only subjective SES predicted positive psychological characteristics. These findings suggest the importance of understanding influences at the individual, family, and neighborhood levels for optimally targeting interventions to reduce health disparities earlier in life.

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)0.006
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.033
GPT teacher head0.393
Teacher spread0.360 · 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

Citations337
Published2006
Admission routes2
Has abstractyes

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