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Record W2803484965 · doi:10.1007/s13178-018-0339-8

Canadian Sex Workers Weigh the Costs and Benefits of Disclosing Their Occupational Status to Health Providers

2018· article· en· W2803484965 on OpenAlexafffundabout
Cecilia Benoit, Michaela Smith, Mikael Jansson, Samantha Magnus, Renay Maurice, Jackson Flagg, Dan Reist

Bibliographic record

VenueSexuality Research and Social Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of Victoria
FundersInstitute of Gender and HealthCanadian Institutes of Health Research
KeywordsSex workersSexual behaviorPsychologyBusinessEnvironmental healthMedicineSocial psychology

Abstract

fetched live from OpenAlex

Prostitution stigma has been shown to negatively affect the work, personal lives, and health of sex workers. Research also shows that sex workers have much higher unmet health care needs than the general population. Less is known about how stigma obstructs their health-seeking behaviors. For our thematic analysis, we explored Canadian sex workers’ accounts ( N = 218) of accessing health care services for work-related health concerns. Results show that participants had mixed feelings about revealing their work status in health care encounters. Those who decided not to disclose were fearful of negative treatment or expressed confidentiality concerns or lack of relevancy. Those who divulged their occupational status to a health provider mainly described benefits, including nonjudgment, relationship building, and comprehensive care, while a minority experienced costs that included judgment, stigma, and inappropriate health care. Overall, health professionals in Canada appear to be doing a good job relating to sex workers who come forward for care. There is still a need for some providers to learn how to better converse with, diagnose, and care for people in sex work jobs that take into account the heavy costs associated with prostitution stigma.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
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 head0.112
GPT teacher head0.460
Teacher spread0.348 · 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

Citations43
Published2018
Admission routes3
Has abstractyes

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