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Record W3124749508 · doi:10.3390/socsci10020035

Job Attributes and Mental Health: A Comparative Study of Sex Work and Hairstyling

2021· article· en· W3124749508 on OpenAlexafffund
Bill McCarthy, Mikael Jansson, Cecilia Benoit

Bibliographic record

VenueSocial Sciences · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsMental healthPsychologySex workSocial psychologyStigma (botany)UnemploymentHostilityPerspective (graphical)Job insecurityWork (physics)MedicinePsychiatryEconomics

Abstract

fetched live from OpenAlex

A growing literature advocates for using a labor perspective to study sex work. According to this approach, sex work involves many of the costs, benefits, and possibilities for exploitation that are common to many jobs. We add to the field with an examination of job attributes and mental health. Our analysis is comparative and uses data from a panel study of people in sex work and hairstyling. We examined job attributes that may differ across these occupations, such as stigma and customer hostility, as well as those that may be more comparable, such as job insecurity, income, and self-employment. Our analysis used mixed-effects regression and included an array of time-varying and time-invariant variables. Our results showed negative associations between mental health and job insecurity and stigma, for both hairstyling and sex work. We also found two occupation-specific relationships: for sex work, limited discretion to make decisions while at work was negatively related to mental health, whereas for hairstyling, mental health was positively associated with self-employment. Our results highlight the usefulness of an inter-occupational labor perspective for understanding the mental health consequences of being in sex work compared to hairstyling.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
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 head0.089
GPT teacher head0.406
Teacher spread0.317 · 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 designQualitative
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

Citations1
Published2021
Admission routes2
Has abstractyes

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