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Work Hours, Work Intensity, and Work Addiction

2009· book-chapter· en· W258828231 on OpenAlexaff
Ronald J. Burke, Lisa Fıksenbaum

Bibliographic record

VenueOxford University Press eBooks · 2009
Typebook-chapter
Languageen
FieldPsychology
TopicWorkaholism, burnout, and well-being
Canadian institutionsYork University
Fundersnot available
KeywordsWork (physics)Work IntensityAutonomyPsychologyWork scheduleWork–life balanceAddictionWorking timeSocial psychologyApplied psychologyPolitical sciencePsychiatryEngineering

Abstract

fetched live from OpenAlex

Abstract This article reviews the literature on the antecedents and consequences of working hours, work intensity, and work addiction particularly among managers and professionals. The dependent variables associated with these include health-related illnesses, injuries, sleep patterns, fatigue, heart rate, and hormone level changes, as well as several work/non-work life balance issues. Motives for working long hours such as joy in work, avoiding job insecurity or negative sanctions from a superior, and employer demands, are addressed in detail, and a multitude of moderators shown to have affected the work hours and well-being relationship are reviewed. These include reasons for working long hours, work schedule autonomy, monetary gain, and choice in working long hours. This article suggests a need for more research to understand better the effects of work hours, work intensity, and workaholism, as well as providing a number of implications and organizational and societal suggestions for addressing work-hour concerns.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.857
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.019
GPT teacher head0.219
Teacher spread0.200 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations15
Published2009
Admission routes1
Has abstractyes

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