Predictors of Retirement Voluntariness Using Canadian Longitudinal Study on Aging Data
Bibliographic record
Abstract
Objectives: Involuntary exit from the labor force can lead to poor health and well-being outcomes. Therefore, the purpose of this research is to better understand the factors that contribute to perceived retirement voluntariness. Methods: We conducted descriptive and multivariable logistic regression analyses using a sample of recent retirees ( n = 2080) from the Canadian Longitudinal Study on Aging (CLSA). Results: More than one-quarter (28%) of older workers perceived their retirement to be involuntary. Among 37 possible predictors, 14 directly predicted retirement voluntariness and many more indirectly predicted retirement voluntariness. Only four direct predictors were common to both women and men, retiring because of organizational restructuring/job elimination; disability, health, or stress; financial possibility; and having wanted to stop working. Discussion: Findings suggest the need for employment support, health promotion, work disability prevention, financial education, and support that is sensitive to the differences between women and men to prevent involuntary retirement.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".