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Record W2096422939 · doi:10.3138/cpp.2014-077

Occupational Stigma and Mental Health: Discrimination and Depression among Front-Line Service Workers

2015· article· en· W2096422939 on OpenAlexaffvenue
Cecilia Benoit, Bill McCarthy, Mikael Jansson

Bibliographic record

VenueCanadian Public Policy · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsStigma (botany)Mental healthSex workPsychologyFront lineDepression (economics)Association (psychology)PsychiatrySocial psychologyMedicinePolitical scienceHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

A large body of research shows a link between stigma and poor health; yet stigma is complex and involves several processes. This paper employs a social determinants of health perspective to shed light on the link between work and one dimension of stigma—discrimination. It examines discrimination and depression with data from a comparative study of three front-line service jobs: sex work, serving food and alcohol, and barbering and hairstyling. Our findings show positive associations between depression and the most highly stigmatized occupation—sex work—and between discrimination and depression. Discrimination mediates part of the association between sex work and depression, and self-worth partially mediates the association between discrimination and depression. Equity policies that improve their social determinants will contribute to better mental health for sex industry workers. Additional strategies aimed at reducing the formidable discrimination linked to their work are also urgently needed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.403
Teacher spread0.304 · 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

Citations44
Published2015
Admission routes2
Has abstractyes

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