CORRELATES OF JOB SATISFACTION AMONG FULL-TIME EMPLOYED OLDER ADULTS WITH CHRONIC CONDITIONS
Bibliographic record
Abstract
Abstract With a rapidly aging workforce and the rising prevalence of chronic conditions, efforts are needed to better understand the needs of older adult workers and how to create supportive working environments for them. This study examines factors associated with job satisfaction among full-time employed adults ages 60+ years with 1+ chronic conditions. Data were collected with an internet-delivered survey in January 2022. Analyses included 337 older adult workers with chronic conditions. An ordinal regression model was fitted to assess factors associated with higher levels of job satisfaction. The model adjusted for sociodemographics, disease characteristics, social engagement, work logistics, and perceptions about the workplace. On average, participants were age 65.14(±4.56) years and self-reported 3.05(±2.09) chronic conditions. Twenty-eight percent reported being very satisfied with their current job and 48.4% worked remotely 1+ days per work week. Higher job satisfaction levels were positively associated with being Hispanic (β=0.68, P=0.030), exhibiting stronger organizational citizenship behavior (β=0.16, P< 0.001), and working more days remotely (β=0.12, P=0.026). Higher job satisfaction levels were negatively associated with job-related stress (β=-0.26, P< 0.001), feelings of social disconnectedness (β=-0.12, P=0.010), and greater intentions of leaving current jobs within the next three months (β=-0.70, P< 0.001). Findings suggest that job satisfaction among older adult workers is rooted in their compatibility with their organizations’ work environment, management of job-related stressors, and opportunity to engage in meaningful and fulfilling interactions with others. Strategies such as remote working are encouraged to give older adults flexibility to promote work-life balance and self-manage their chronic conditions.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".