Perceived Control and Risk Characteristics as Predictors of Older Adults' Health Risk Estimates
Bibliographic record
Abstract
Overestimating one's own health risks is associated with negative affect and decreased well-being. To identify psychosocial factors that reduce pessimistic risk estimates, the authors examined global perceived (primary and secondary) control as a predictor of health risk (hip fracture) estimates among 222 community-dwelling older adults. To determine whether characteristics of the health risk moderated the effects of perceived control on risk estimates, the authors manipulated risk level (low, high) and risk attribution (controllable, uncontrollable). The effects of perceived control differed as a function of risk attribution: Regardless of implied risk level, perceived primary control significantly predicted lower risk estimates in the controllable condition but not in the uncontrollable condition. In contrast, perceived secondary control significantly predicted lower risk estimates in the uncontrollable condition but not in the controllable condition, emphasizing its importance when direct influence is not feasible. The authors discuss implications for anticipating how older adults estimate their health risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".