Self-reported experiences of health services among female street-based prostitutes: a cross-sectional survey.
Bibliographic record
Abstract
BACKGROUND: Previous studies show that women working in prostitution do not use routine health services appropriately. Little is known about the nature and frequency of service contacts or barriers to access. This information is needed if use of current services by this group is to improve. AIM: To identify barriers reducing access to health services by street prostitutes, and to identify current patterns of use. DESIGN OF STUDY: Cross-sectional survey. SETTING: Inner-city Bristol. METHOD: Seventy-one female street-based prostitutes were interviewed about their experiences of health services. RESULTS: The women had frequent contacts with healthcare providers. The general practitioner (GP) was the main source of all types of care. Although 83% (59/71) were registered with a GP, 62% (36/59) had not disclosed their work. Only 46% (33/71) had been screened for sexually transmitted infection in the previous year and 24% (17/71) were vaccinated against hepatitis B, a national recommendation for sex workers. Only 38% (25/65) had had cervical smears according to screening guidelines. Opportunistic screening and care was important. While pregnant with their last child, only 30% (14/47) booked in the first trimester and attended all antenatal appointments, with 13% (6/47) receiving no antenatal care until admitted in labour. Appointments, waiting times, and fear of judgement and other patients staring, were considered significant barriers to service use. The model suggested by the women was an integrated service providing basic living needs alongside health care. CONCLUSION: Non-disclosure and poor attendance for follow-up make appropriate care difficult, and may contribute to poor health. Despite frequent service contacts, opportunities for care are being missed.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".