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Record W1989680493 · doi:10.2190/hs.41.1.c

Work or Place? Assessing the Concurrent Effects of Workplace Exploitation and Area-of-Residence Economic Inequality on Individual Health

2010· article· en· W1989680493 on OpenAlexaff
Carles Muntaner, Yong Li, Edwin Ng, Joan Benach, Haejoo Chung

Bibliographic record

VenueInternational Journal of Health Services · 2010
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsResidenceInequalitySocial determinants of healthPsychological interventionHealth equitySocial inequalityPsychologyDemographic economicsEnvironmental healthPublic healthGerontologyMedicineNursingEconomics

Abstract

fetched live from OpenAlex

Building on previous multilevel studies in social epidemiology, this cross-sectional study examines, simultaneously, the contextual effects of workplace exploitation and area-of-residence economic inequality on social inequalities in health among low-income nursing assistants. A total of 868 nursing assistants recruited from 55 nursing homes in Kentucky, Ohio, and West Virginia were surveyed between 1999 and 2001. Using a cross-classified multilevel design, the authors tested the effects of area-of-residence (income inequality and racial segregation), workplace (type of nursing home ownership and managerial pressure), and individual-level (age, gender, race/ethnicity, health insurance, length of employment, social support, type of nursing unit, preexisting psychopathology, physical health, education, and income) variables on health (self-reported health and activity limitations) and behavioral outcomes (alcohol use and caffeine consumption). Findings reveal that overall health was associated with both workplace exploitation and area-of-residence income inequality; area of residence was associated with activity limitations and binge drinking; and workplace exploitation was associated with caffeine consumption. This study explicitly accounts for the multiple contextual structure and effects of economic inequality on health. More work is necessary to replicate the current findings and establish robust conclusions on workplace and area of residence that might help inform interventions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.075
GPT teacher head0.469
Teacher spread0.394 · 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.

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

Citations19
Published2010
Admission routes1
Has abstractyes

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