Retirement Decisions of People with Disabilities: Voluntary or Involuntary
Bibliographic record
Abstract
While some retirement is welcomed and on-time, other retirements are involuntary or forced due to the loss of a job, an early retirement incentive, a health problem, mandatory retirement, lack of control with too many job strains, or to provide care to a family member. An analysis of the 2002 Canadian General Social Survey reveals that 27% of retirees retired involuntarily. This research focuses on the disabled population in Canada and considers factors that influence voluntary and involuntary retirement. Further, consideration is given to the economic consequences of retiring involuntarily. This research will examine issues surrounding retirement and disability through statistical analysis of the Canadian Participation and Activity Limitations Survey (PALS) 2006 data. Methods include the use of descriptive statistics and logistic regression analysis to determine the characteristics associated with involuntary retirement. This study found that those who retired involuntarily were more likely to have the following socio-demographic and socio-economic characteristics: age 55 or less, less than high school education, live in Quebec, rent their home, and have relatively low income. They were also more likely to be worse off financially after retirement and to be receiving social assistance or a disability benefit. In terms of disability, the likelihood of retiring involuntarily was greater for those with poor health at retirement, the age of onset was over 55, higher level of severity, and multiple types of disability. For the discussion, a social inequalities framework is used, where health selection into involuntary retirement depends on social location defined by age and education. Policy initiatives that reduce the effects of disability, and allow individuals to remain in or return to the labour force such as workplace accommodations are discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".