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Record W2165808800 · doi:10.1177/0956797611419170

Pathways to Resilience

2011· article· en· W2165808800 on OpenAlexaff
Gregory E. Miller, Margie E. Lachman, Edith Chen, Tara L. Gruenewald, Arun S. Karlamangla, Teresa E. Seeman

Bibliographic record

VenuePsychological Science · 2011
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institute on Aging
KeywordsPsychologyResilience (materials science)Cognitive psychologyCognitive science

Abstract

fetched live from OpenAlex

Children raised in families with low socioeconomic status (SES) go on to have high rates of chronic illness in adulthood. However, a sizable minority of low-SES children remain healthy across the life course, which raises questions about the factors associated with, and potentially responsible for, such resilience. Using a sample of 1,205 middle-aged Americans, we explored whether two characteristics--upward socioeconomic mobility and early parental nurturance--were associated with resilience to the health effects of childhood disadvantage. The primary outcome in our analyses was the presence of metabolic syndrome in adulthood. Results revealed that low childhood SES was associated with higher prevalence of metabolic syndrome at midlife, independently of traditional risk factors. Despite this pattern, half the participants raised in low-SES households were free of metabolic syndrome at midlife. Upward social mobility was not associated with resilience to metabolic syndrome. However, results were consistent with a buffering scenario, in which high levels of maternal nurturance offset the metabolic consequences of childhood disadvantage.

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)0.003
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.153
GPT teacher head0.461
Teacher spread0.309 · 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

Citations197
Published2011
Admission routes1
Has abstractyes

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