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Record W645346557

Forty Hours Doesn't Work for Everyone: Examining Employee Preferences for Work Hours

2008· article· en· W645346557 on OpenAlexfundno aff
Lindsey A. Zahn, Michael C. Sturman

Bibliographic record

VenueCornell Peter and Stephanie Nolan School of Hotel Administration (Cornell University) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
FundersKillam Trusts
KeywordsWork hoursWorkforceEconomic shortageWork (physics)Job satisfactionWorking hoursPsychologyDemographic economicsSocial psychologyLabour economicsEconomicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

Current economic conditions have caused many employers to reduce employees’ work hours—a trend that will likely continue if the economy worsens. Yet research on work hours is limited, as most studies in this area have focused on the effects of employees’ working in excess of a 40-hour work week. This report seeks to specifically examine the effect of “hours mismatch,” which is defined as the mismatch between the number of hours the employee desires to work and the actual number of hours worked. Based on a study of 1,032 individuals, the results show that hours mismatch is an important predictor of attitudinal outcomes, including life satisfaction, work-family conflict, job stress, and intent to turn over. Moreover, the measurement of difference is generally more predictive than simply measuring hours worked. The results show that working either more than the desired hours or less than desired hours has effects on attitudes like job stress, intent to turn over, and life satisfaction. Although employees disliked working “over hours,” a substantial shortage of work hours was far worse. Although employers may face the need to reduce workers’ hours, this study suggests the importance of taking into account workers’ preferences when determining work schedules, or at least understanding the kind of psychological impact that reduced hours will have on their workforce.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.098
GPT teacher head0.261
Teacher spread0.163 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2008
Admission routes1
Has abstractyes

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