Levels of and Changes in Life Satisfaction Predict Mortality Hazards:\nDisentangling the Role of Physical Health, Perceived Control, and Social\nOrientation
Bibliographic record
Abstract
It is well-documented that well-being typically evinces precipitous\ndecrements at the end of life. However, research has primarily taken a\npostdictive approach by knowing the outcome (date of death)\nand aligning in retrospect how well-being has changed for people with documented\ndeath events. In the present study, we made use of a predictive\napproach by examining whether and how levels of and\nchanges in life satisfaction prospectively predict\nmortality hazards and delineate the role of contributing factors, including\nhealth, perceived control, and social orientation. To do so, we applied shared\nparameter growth-survival models to 20-year longitudinal data from 10,597\nparticipants (n = 1,560 or 15% deceased; age at\nbaseline: M = 44 years, SD =\n17, range: 18–98 years) from the national German\nSocio-Economic Panel Study (SOEP). Our findings showed that lower levels and\nsteeper declines of life satisfaction each uniquely predicted higher mortality\nrisks. Results also reveal moderating effects of age and perceived control: Life\nsatisfaction levels and changes had stronger predictive effects for mortality\nhazards among older adults. Perceived control is associated with lower mortality\nhazards; however, this effect is diminished for those who experience accelerated\nlife satisfaction decline. Variance decomposition suggests that predictive\neffects of life satisfaction trajectories were partially unique\n(3–6%) and partially shared with physical health, perceived\ncontrol, and social orientation (17–19 %). Our discussion\nfocuses on the strengths and challenges of a predictive approach to link\ndevelopmental changes (in life satisfaction) to mortality hazards and considers\nimplications of our findings for healthy aging.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| 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".