Access to dental care and self-perception of oral health in adult women sex workers: scoping review
Bibliographic record
Abstract
Objective: To recognize the association between access to dental care and self-perception of oral health in terms of subjective assessment as good, regular/average, or bad/poor, in adult women sex workers. Methods: A scoping review during October 2022 in EMBASE (Elsevier), LILACS and PUBMED. This study takes into consideration The Joanna Briggs Institute (JBI) method guide and the PRISMA extension for Scoping Reviews (PRISMA-ScR). The current study also includes cross-sectional studies that considered variables like sex workers, access to dental care, and self-perceived health. The quality of the studies was evaluated using the New Castle-Ottawa tool. Results: Forty-eight articles were identified, of which three were finally included in the synthesis of the results. The population of the included studies was focused on sex workers from different places such as Switzerland, China and India, the issue of access to dental care services and self-perception of health was also common addressing sexual health issues, these studies identified economic, social, and cultural barriers in access to dental care for sex workers; also, their self-perceived health was interpreted as good. Conclusion: The limited amount of information regarding access to oral health services by sex workers, regarding information found in terms of sexual health, was evidenced; for this reason, it is important to carry out more studies that consider the oral health component in sex workers, considering the risks and vulnerabilities to which they are exposed.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".