Protective Factors for Adults From Low-Childhood Socioeconomic Circumstances
Bibliographic record
Abstract
OBJECTIVE: Low socioeconomic status (SES) early in life is one of the most well-established social predictors of poor health. However, little is understood about why some adults who grew up in low-SES environments do not have poor health outcomes. This study examined whether the psychological characteristic of "shift-and-persist" protects adults from the physiological risks of growing up in low-SES households. Shift-and-persist consists of reframing appraisals of current stressors more positively (shifting), while simultaneously persisting with a focus on the future. We hypothesized that this characteristic would be associated with reduced physiological risk in low-childhood SES individuals. METHODS: A national sample of 1207 adults (aged 25-74 years) from the Survey of Midlife Development in the United States completed psychological questionnaires and were queried about parent education. Biologic assessments consisted of 24 different measures across seven physiological systems, from which a composite measure representing cumulative physiological risk (allostatic load) was derived. RESULTS: Among adults who grew up in low-SES households, those who engaged in high-shift-and-high-persist strategies had the lowest allostatic load (b = -0.15, p = .04). No benefit of shift-and-persist was found for those from higher-childhood SES backgrounds (p = .36). CONCLUSIONS: Identifying the health-related protective qualities that naturally occur in some low-SES individuals represents one important approach for developing future health improvement interventions for those who start out life low in SES. Moreover, the psychological qualities that are protective from future disease risk for those from low-SES backgrounds are different from those beneficial to high-SES individuals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".