The Paradoxical Role of Perceived Control in Late Life Health Behavior
Bibliographic record
Abstract
Research has established the health benefits of psychological factors, including the way individuals appraise outcomes. Although many studies confirm that appraising outcomes as controllable is adaptive for health, a paradoxical possibility is largely ignored: Perceived control may be detrimental under some conditions. Our premise was that appraising health as controllable but at the same time ascribing little value to it might signal a dysfunctional psychological mindset that fosters a mistaken sense of invincibility. During face-to-face interviews with a representative sample of older adults (age range = 72-99), we identified individuals with such a potentially maladaptive "invincible" mindset (high perceived control and low health value) and compared them to their counterparts on several outcomes. The findings were consistent with our hypotheses. The invincibles denied future risks, they lacked the activating emotion of fear, and they visited their physicians less often over a subsequent five-year period. Moreover, in contrast to their counterparts, the invincibles did not appear strategic in their approach to seeking care: Even poor health did not prompt them to seek the counsel of a physician. The recognition that psychological appraisals are modifiable highlights the promise of remedial methods to alter maladaptive mindsets, potentially improving quality of life.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".