From discrimination and dis-ease to aging and disease-an epigenetic connection
Bibliographic record
Abstract
The three pillars of an effective, safe, and successful society are equity, diversity, and inclusion (EDI).Equity denotes fair access to and the provision of opportunities to everyone.Diversity reflects and celebrates the rich and unique range of human differences, and inclusion signifies the importance of valuing, respecting, and empowering everyone across all societies and cultures.Although global EDI efforts are expanding, discrimination is still a challenge for large groups in our society, such as women, immigrants, elderly people, racial minorities, lower-income persons, people with disabilities, sexual and gender minorities, and people experiencing addiction and mental health challenges.1,2 Discrimination is a multifaceted issue that affects individuals' health in many ways.It limits access to healthcare resources, lowering overall quality of life, and is a stressor and social determinant of health that causes adverse effects through the direct physiologic impact of stress that may later manifest as disease.Although analyses of discrimination and its health outcomes are still emerging, the research accumulated over the past few decades suggests that discrimination is a powerful stressor.[1][2][3] While short-term physiologic reactions to acute stress are often adaptive, persistent chronic stress causes deleterious outcomes.Discrimination is a chronic, ongoing, and unpredictable stressor that activates numerous cascades of stress-related emotional, physiological, and behavioral changes.Mechanistically, the hypothalamic-pituitary-adrenal (HPA) axis is associated with, governs, and responds to stress.Physiologically, constant and aberrant activation of the HPA axis due to chronic stress affects metabolism and is implicated in the inflammatory responses of many general and mental health issues.For example, elevated HPA axis activity has been associated with race-, sex-, and weight-related discrimination.4 Furthermore,
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".