Getting the Hours You Want in the Preretirement Years: Work Hour Preferences and Mismatch Among Older Canadian Workers
Bibliographic record
Abstract
Expectations regarding the work hours of older workers have changed over time. This article examines Canadian workers in their pre-retirement years to identify patterns in work hour preferences by gender—and whether work hour mismatch predicts late-stage workforce transitions. Findings from a national sample of Canadian workers show that slightly over half of all respondents were content with the number of hours they worked, but that 36% of the sample expressed a preference to work fewer hours and more than 8% expressed a preference to work more hours. Among men and women there were remarkable similarities in the factors that predicted a mismatch between respondents’ preferred and actual hours worked. While highlighting heterogeneity in the work hour preferences of Canadian workers in the years leading up to traditional retirement age, findings illustrate how mismatches between workers’ preferred and actual work hours predict later career workforce transitions. Findings also emphasize the importance of good relations with coworkers and supervisor support as factors that can enhance preferences to continue working at later career stages. Our findings also support claims that employers ought to be encouraged to focus on later career transitions and to find opportunities to enhance the fit between the number of hours required to meet work demands with individuals’ capabilities and interests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".