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Record W2088827718 · doi:10.12927/hcpol.2013.23597

Exploring Stigma by Association among Front-Line Care Providers Serving Sex Workers

2013· article· en· W2088827718 on OpenAlexaffvenue
Rachel Phillips, Cecilia Benoit

Bibliographic record

VenueHealthcare policy · 2013
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFront lineHealth careStigma (botany)Front (military)SociologyPublic relationsPolitical scienceMedicineLawEngineering

Abstract

fetched live from OpenAlex

Stigma by association, also referred to as "courtesy stigma," involves public disapproval evoked as a consequence of associating with stigmatized persons. While a small number of sociological studies have shown how stigma by association limits the social support and social opportunities available to family members, there is a paucity of research examining this phenomenon among the large network of persons who provide health and social services to stigmatized groups. This paper presents results from a primarily qualitative study of the work-place experiences of a purposive sample of staff from an organization providing services to sex workers. The findings suggest that stigma by association has an impact on staff health because it shapes both the workplace environment as well as staff perceptions of others' support. At the same time, it is evident that some staff, owing to their more advantaged social location, are better able to manage courtesy stigma than others.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.004
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
GPT teacher head0.405
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations21
Published2013
Admission routes2
Has abstractyes

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