Mastering the canvas of life: Identifying the antecedents of sense of control using a lagged exposure‐wide approach
Bibliographic record
Abstract
Abstract Accumulating studies have documented strong associations between a higher sense of control and improved health and well‐being outcomes. However, less is known about the determinants of increased sense of control. Our analysis used data from 13,771 older adults in the Health and Retirement Study (HRS)—a diverse, longitudinal, and national study of adults aged >50 in the United States. Using generalized linear regression models, with a lagged exposure‐wide approach, we evaluated how changes in 59 predictors (i.e., physical health, health behavior, and psychosocial factors) over a 4‐year period (between t 0 ;2006/2008 and t 1 ;2010/2012) might lead to changes in sense of control another 4‐years later (t 2 ;2014/2016). After adjusting for a rich set of baseline covariates, changes in some health behaviors (e.g., sleep problems), physical health conditions (e.g., physical functioning limitations, eyesight), and psychosocial factors (e.g., positive affect, purpose in life) were associated with changes in sense of control four years later. However, there was little evidence that other factors were associated with a subsequent sense of control. A key challenge in advancing intervention development is the identification of antecedents that predict a sense of control. Our results identified several novel targets for interventions and policies aimed at increasing a sense of control.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".