Bibliographic record
Abstract
Some individuals, despite facing recurrent, severe adversities in life such as low socioeconomic status (SES), are nonetheless able to maintain good physical health. This article explores why these individuals deviate from the expected association of low SES and poor health and outlines a "shift-and-persist" model to explain the psychobiological mechanisms involved. This model proposes that, in the midst of adversity, some children find role models who teach them to trust others, better regulate their emotions, and focus on their futures. Over a lifetime, these low-SES children develop an approach to life that prioritizes shifting oneself (accepting stress for what it is and adapting the self through reappraisals) in combination with persisting (enduring life with strength by holding on to meaning and optimism). This combination of shift-and-persist strategies mitigates sympathetic-nervous-system and hypothalamic-pituitary-adrenocortical responses to the barrage of stressors that low-SES individuals confront. This tendency vectors individuals off the trajectory to chronic disease by forestalling pathogenic sequelae of stress reactivity, like insulin resistance, high blood pressure, and systemic inflammation. We outline evidence for the model and argue that efforts to identify resilience-promoting processes are important in this economic climate, given limited resources for improving the financial circumstances of disadvantaged 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".