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Record W2039516986 · doi:10.1177/0022146514568348

Different Contexts, Different Effects?

2015· article· en· W2039516986 on OpenAlexaff
Sibyl Kleiner, Reinhard Schunck, Klaus Schömann

Bibliographic record

VenueJournal of Health and Social Behavior · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Calgary
FundersBremen International Graduate School of Social SciencesDeutscher Akademischer Austauschdienst
KeywordsGermanMental healthIncentiveSocioeconomic statusWork (physics)PsychologyWork hoursDemographic economicsGerontologyDemographyMedicineSociologyPsychiatryGeographyEconomics

Abstract

fetched live from OpenAlex

This paper takes a comparative approach to the topic of work time and health, asking whether weekly work hours matter for mental health. We hypothesize that these relationships differ within the United States and Germany, given the more regulated work time environments within Germany and the greater incentives to work long hours in the United States. We further hypothesize that German women will experience greatest penalties to long hours. We use data from the German Socioeconomic Panel and the National Longitudinal Survey of Youth to examine hours effects on mental health score at midlife. The results support our initial hypothesis. In Germany, longer work time is associated with worse mental health, while in the United States, as seen in previous research, the associations are more complex. Our results do not show greater mental health penalties for German women and suggest instead a selection effect into work hours operating by gender.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.009
Scholarly communication0.0060.010
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.110
GPT teacher head0.457
Teacher spread0.348 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations31
Published2015
Admission routes1
Has abstractyes

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