Longitudinal Associations of Job Demand‐Control Characteristics With Objective and Subjective Cognitive Outcomes in Older Workers: The Health and Retirement Study
Bibliographic record
Abstract
BACKGROUND: The job demand-control (JDC) psychosocial work model has demonstrated effects on objective cognitive performance, but its association with subjective memory is still poorly understood. We examined longitudinal associations of JDC characteristics with objective (general cognitive function/episodic memory) and subjective (perceived memory) cognition. METHODS: Using the Health and Retirement Study (HRS) data, 3497 workers aged 50+ were followed from 2006-2008 to 2018. Self-reported job demand and job control were dichotomized and later combined into quadrants reflecting high/low job strain and active/passive jobs. Analyses used covariate-adjusted generalized estimating equations. RESULTS: High job control was significantly positively associated with general cognitive function (regression coefficient: 0.409, p < 0.001), episodic memory (0.373, p < 0.001), and subjective memory (0.057, p = 0.034). "Low demand and high control" (low strain work) exhibited significant, positive associations with all cognitive outcomes. "High demand and high control" (active work) was significantly positively associated with objective cognitive outcomes. CONCLUSIONS: Opportunities for enhancing job control may promote objective cognition and subjective memory health in the US aging workforce.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".