A Review of the Relationship between Socioeconomic Status Change and Health
Bibliographic record
Abstract
OBJECTIVES: This review aimed to identify and synthesize the existing literature on the effects of socioeconomic status (SES) changes on health. METHODS: A review was conducted using Medline, Cochrane library, and CINAHL (Cumulative Index to Nursing and Allied Health Literature). All longitudinal or cross-sectional studies that examined links between changes to SES across different time periods and measured health outcomes were included. Screening was conducted using select inclusion and exclusion criteria in order of title, abstract, and full text. Two independent reviewers assessed the quality of the full text articles using the Downs and Black checklist. RESULTS: Our literature search led to 2719 peer reviewed articles, 2639 of which were title screened after duplicates were removed. A total of 117 abstracts and 12 full text articles were screened. Overall, findings from 11 articles form the basis of this review. Eight different types of measures of changes to SES were identified. These include education, occupation, economic security, income sufficiency, home ownership, car ownership, health insurance, and marital status. Assessed outcomes included measures related to physical health, cardiovascular disease, mental health, and oral health. A large proportion of studies found that an SES change impacts health. Evidence suggests that those with consistently high SES have the best health outcomes, followed by those who report their SES change from low to high (upward social mobility). Evidence on the relative health effects for those who report their SES change from high to low (downward social mobility) compared to those who report consistently low SES is inconsistent. CONCLUSION: Current evidence suggests that an SES change has an impact on an individual's health. More research on the effects of SES changes on health outcomes in adulthood is needed and can inform various areas of health research including health resiliency and development. Future studies should focus on individual SES indicators and their effects on health outcomes at multiple points throughout life.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".