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Record W2791126555 · doi:10.1177/0022146517754091

The Status–Health Paradox: Organizational Context, Stress Exposure, and Well-being in the Legal Profession

2018· article· en· W2791126555 on OpenAlexafffundabout
Jonathan Koltai, Scott Schieman, Ronit Dinovitzer

Bibliographic record

VenueJournal of Health and Social Behavior · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsStressorDisadvantageOverworkContext (archaeology)Demographic economicsMental healthInequalityPsychologyPolitical scienceSocial psychologyLabour economicsEconomicsClinical psychologyPsychiatryLaw

Abstract

fetched live from OpenAlex

Prior research evaluates the health effects of higher status attainment by analyzing highly similar individuals whose circumstances differ after some experience a "status boost." Advancing that research, we assess health differences across organizational contexts among two national samples of lawyers who were admitted to the bar in the same year in their respective countries. We find that higher-status lawyers in large firms report more depression than lower-status lawyers, poorer health in the American survey, and no health advantage in Canada. Adjusting for income exacerbates these patterns-were it not for their higher incomes, large-firm lawyers would have a greater health disadvantage. Last, we identify two stressors in the legal profession, overwork and work-life conflict, that are more prevalent in the private sector and increase with firm size. Adjusting for these stressors explains well-being differences across organizational contexts. This study documents the role of countervailing mechanisms in health inequality research.

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)0.002
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 score0.067
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.421
Teacher spread0.380 · 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

Citations18
Published2018
Admission routes3
Has abstractyes

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