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Record W2925494309 · doi:10.1093/workar/way015

Getting the Hours You Want in the Preretirement Years: Work Hour Preferences and Mismatch Among Older Canadian Workers

2018· article· en· W2925494309 on OpenAlexaffabout
Michelle Pannor Silver, Jason Settels, Markus H. Schafer, Scott Schieman

Bibliographic record

VenueWork Aging and Retirement · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of TorontoThe Scarborough Hospital
Fundersnot available
KeywordsWorkforcePreferenceWork (physics)Work hoursDemographic economicsSample (material)PsychologySupervisorWorking hoursSocial psychologyLabour economicsManagementEconomicsEconomic growthEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.347
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2018
Admission routes2
Has abstractyes

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