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Record W2606969471 · doi:10.2196/jmir.6746

A Content Analysis of Health and Safety Communications Among Internet-Based Sex Work Advertisements: Important Information for Public Health

2017· article· en· W2606969471 on OpenAlexafffundabout
Julie Ann Kille, Vicky Bungay, John L. Oliffe, Chris Atchison

Bibliographic record

VenueJournal of Medical Internet Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of VictoriaUniversity of British ColumbiaProvidence Health Care
FundersCanadian Institutes of Health ResearchUniversity of British Columbia
KeywordsThematic analysisThe InternetPsychological interventioneHealthPopulationSex workReproductive healthContent analysisPublic healthEthnic groupAdvertisingPsychologyMedicineBusinessEnvironmental healthSociologyQualitative researchPolitical scienceHealth careNursingFamily medicineSocial science

Abstract

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BACKGROUND: The capacity to advertise via the Internet continues to contribute to the shifting dynamics in adult commercial sex work. eHealth interventions have shown promise to promote Internet-based sex workers' health and safety internationally, yet minimal attention has been paid in Canada to developing such interventions. Understanding the information communicated in Internet-based sex work advertisements is a critical step in knowledge development to inform such interventions. OBJECTIVE: The purpose of this content analysis was to increase our understanding of the health and safety information within the Internet advertisements among women, men, and transgender sex workers and to describe how this information may be utilized to inform eHealth service development for this population. METHODS: A total of 75 Internet-based sex worker advertisements (45 women, 24 men, and 6 transgender persons) were purposefully selected from 226 advertisements collected as part of a larger study in Western Canada. Content analysis was employed to guide data extraction about demographic characteristics, sexual services provided, service restrictions, health practices and concerns, safety and security, and business practices. Frequencies for each variable were calculated and further classified by gender. Thematic analysis was then undertaken to situate the communications within the social and commercialized contexts of the sex industry. RESULTS: Four communications themes were identified: (1) demographic characteristics; (2) sexual services; (3) health; and (4) safety and security. White was the most common ethnicity (46/75, 61%) of advertisements. It was found that 20-29 years of age accounted for 32 of the 51 advertisements that provided age. Escort, the only legal business title, was the most common role title used (48/75, 64%). In total, 85% (64/75) of advertisements detailed lists of sexual services provided and 41% (31/75) of advertisements noted never offering uncovered services (ie, no condom). Gender and the type of Web-based platform mattered for information communicated. It was found that 35 of the 45 women's advertisements were situated in personal websites and hosted details about nonsexual aspects of an appointment. Men and transworkers used Internet classified advertisement platforms with predetermined categories. Communications about sexually transmitted infections (STIs) occurred in only 16% (12/75) of advertisements with men accounting for 7. Women's advertisements accounted for 26 of the 37 advertisements noting safety restrictions. Zero men or transpersons restricted alcohol or drug use. In total, 75% (56/75) of advertisements offered out-call services and the average minimal hourly rate ranged from Can $140/h to Can $200/h. CONCLUSIONS: The study findings contribute to understandings about the diverse platforms used in commercial sex advertisements, and how sex workers frame information for potential clients. This information affords health care providers and policy makers insights to how they might assist with promoting the health of Internet-based sex workers and their clients.

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)0.014
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.013
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.235
GPT teacher head0.497
Teacher spread0.262 · 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 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

Citations23
Published2017
Admission routes3
Has abstractyes

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