Contributors to posttraumatic stress symptoms in women sex workers.
Bibliographic record
Abstract
Previous research has demonstrated that women who sell sex (women sex workers [WSWs]) consistently report high levels of posttraumatic stress symptoms. The present study explores multiple factors that may contribute to the variation in WSWs' experiences of posttraumatic stress symptoms, including workers' racial identity, experiences of discrimination and control over their working conditions, the site of selling sex, and their clients' perceived sexual entitlement and violence. The study sample consisted of 314 self-identified WSWs. Online invitations to participate in a 30-min survey were sent to WSWs in the United States and Canada who advertise their services online on sites such as Facebook, the Erotic Review, and Backpages. The hypothesized structure of associations between the variables was tested using structural equation modeling. The model accounted for 68% of the variation in the traumatic stress reported, with direct and indirect effects for workers' racial identity, the site where they sell sex, and experiences of discrimination, especially by police. Clients' violence, on the contrary, was indirectly associated with traumatic stress, as violent clients were also significantly more likely to be perceived as sexually entitled, which, in turn, was the strongest predictor of higher traumatic stress. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".