Connecting social environment variables to the onset of major specific health outcomes
Bibliographic record
Abstract
OBJECTIVE: The present research examined the effects of the social environment on the onset of specific health ailments. DESIGN: Using data from the Health and Retirement Study, we examined participants' responses to social environment questions in 2006 as predictors of onset of different health conditions over the next four years. MAIN OUTCOME MEASURES: Healthy participants (n = 7514) reported on their number of social partners, interaction frequency, positive social support and negative social support with respect to both their family and friends. These variables were used to predict onset of seven conditions in 2010: high blood pressure, heart condition, lung disease, cancer, stroke, diabetes and arthritis. RESULTS: Logistic regressions indicated that the social environment provided some predictive value for onset of most health outcomes, with more positive and less negative social support appearing to buffer against onset. Social environmental variables related to friendships appeared to play a greater role than the family indicators. However, no variable proved universally adaptive, and social indicators had little value in predicting onset of chronic conditions. CONCLUSION: The current findings point to the potential for the social environment to influence later health, while demonstrating the nuanced role that our social lives play with respect to health.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".