Retirement Voluntariness: Meaning, Measurement and Predictors
Bibliographic record
Abstract
The overall purpose of this doctoral thesis was to build a better understanding of the factors that contribute to involuntary retirement in Canada and elsewhere. The goal was to inform further research as well as policy and practice in prevention efforts. Three different studies were conducted, each guided by conceptual frameworks of retirement and the life course perspective. First, given the lack of clarity surrounding the concept of “retirement voluntariness”, the meaning and measurement of the term were examined. An array of terminology, definitions, and measures of retirement voluntariness were identified, which were categorized into two main conceptualizations: the perception of agency in the decision to retire; and the conditions that impact the decision to retire. Second, the literature was systematically scanned for factors explaining perceived involuntary retirement. While a limited number of articles were identified (n=9) some common factors were found such as disability and poor health status (n=7), younger age at retirement (n=5), and job loss (n=4). Third, explanatory variables were taken from those identified in the systematic literature and from existing theoretical models to develop a more comprehensive theoretical model of 54 predictors across seven domains. This model was then employed to conduct sex-disaggregated descriptive and multivariable logistic regression analysis using nationally representative longitudinal data from the Canadian Longitudinal Study on Aging (CLSA). Over one quarter (28%) of older workers who retired between baseline and follow-up one of the CLSA perceived their retirement to be involuntary. Among 37 possible predictors, across seven domains, 14 factors predicted perceived involuntary retirement in this population. Only four predictors were common to both women and men, i.e., retiring because of organizational restructuring, or disability, health, or stress for which odds were greater, and retiring because it was financially possible or because of wanting to stop working which had lower odds. Findings suggest the need for interventions to prevent work disability, financial education, and employment supports for those facing job loss. Further, interventions should be sensitive to the differences between women and men. Suggestions for further research as well as additional recommendations for the prevention of perceived involuntary retirement were offered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".