Subjective socioeconomic status and adolescent health: A meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: To comprehensively and quantitatively examine the association between subjective socioeconomic status (SES) and health outcomes during adolescence. METHODS: Forty-four studies met criteria for inclusion in the meta-analysis. Information on study quality, demographics, subjective SES, health outcomes, and covariates were extracted from each study. Fisher's Z was selected as the common effect size metric across studies. Random-effect meta-analytic models were employed and fail-safe numbers were generated to address publication bias. RESULTS: Overall, subjective SES was associated with health during adolescence (Fisher's Z = .10). The magnitude of the effect varied by type of health outcome, with larger effects observed for mental health outcomes, self-rated health, and general health symptoms; and nonsignificant effects observed for biomarkers of health and substance-use-related health behaviors. Of the measures of subjective SES employed in the reviewed studies, perception of financial constraints, was most strongly associated with adolescent health outcomes. Analysis of covariates indicated that inclusion of objective SES covariates did not affect the association between subjective SES and health. CONCLUSIONS: This meta-analysis has implications for the measurement of subjective SES in adolescents, for the conceptualization of subjective and objective SES, and for the pathways between SES and health in adolescents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.031 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".