PEOPLE DO NOT AGE IN LABS: SOCIAL AND BEHAVIORAL CONSIDERATIONS WHEN DEVELOPING GEROSCIENCE INTERVENTIONS
Bibliographic record
Abstract
Abstract Geroscience researchers are determined to identify the constellation of genetic, molecular, and cellular mechanisms of aging that, eventually, will be translated into anti-aging therapies and pills. It is argued, however, that without a consideration of social and behavioural determinants of health, geroscientists will miss the full potential to impact health and longevity of all humans, not just a select few. Social and behavioural determinants of health are the non-medical, non-biological factors influencing health and disease, and their underlying biological processes. Humans are complicated species, with a lifetime of exposures to stressful, adverse, and sometimes traumatic experiences and a lifetime of engaging in a mix of healthy and unhealthy behaviours. Together, social and behavioural factors shape biological aging through epigenetic embedding and wear and tear of biological systems, causing eventual accelerated biological aging and disease. By understanding what social and behavioural factors are, how they embed biologically and accelerate aging processes, geroscientists can develop a deeper understanding of the complicated nature of human experimental research. Social and behavioural factors impact recruitment, engagement, and adherence of participants in intervention trials, which can obscure significant treatment effects. Social and behavioural factors can also limit treatment effects, when considering how difficult it might be to reverse some of the long-term damage caused by life itself. It is important then, for geroscientists to learn about social and behavioural factors, and the best practices to engage as many people across the wide diversity of life circumstances, to impact the healthspan and lifespan of all.
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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 | 0.050 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".