Accumulation of Biological and Behavioral Data of Female Sex Workers Using Respondent-Driven Sampling: Protocol for a Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Respondent-driven sampling (RDS) is a nonprobability sampling technique that allows the extrapolation of its outcome to the target population. This approach is typically used to overcome the difficulties in studying hidden or difficult-to-reach groups. OBJECTIVE The purpose of this protocol is to generate a systematic review on the accumulation of biological and behavioral data of female sex workers (FSWs) through different surveys that use the RDS method from around the world in the near future. The future systematic review will discuss the initiation, actualization, and problems of RDS during the accumulation of biological and behavioral data of FSWs through surveys from around the world. METHODS The behavior and biological data of FSWs will be extracted from peer-reviewed studies published between 2010 and 2022 and that are acquired through RDS. Using PubMed, Google Scholar, the Cochrane database, Scopus, Science Direct, and the Global Health network, all papers that are available will be obtained using the search phrases “respondent-driven” and “Female Sex Workers” OR “FSW” OR “sex workers” OR “SW.” According to STROBE-RDS (Strengthening the Reporting of Observational Studies in Epidemiology for Respondent-Driven Sampling) criteria, the data will be retrieved through a data extraction form and will be organized using World Health Organization classifications of areas. The Newcastle-Ottawa Quality Assessment Scale will be used to measure bias risk and overall study quality. RESULTS The future systematic review that will be generated from this protocol will offer evidence for or against the claim that using the RDS technique to recruit participants from “hidden” or “hard-to-reach” populations is the best strategy. The results will be disseminated through a peer-reviewed publication. Data collection started on April 1, 2023, and the systematic review is expected to be published by December 15, 2023. CONCLUSIONS A minimum set of parameters for specific methodological, analytical, and testing procedures, including RDS methods to evaluate the overall quality of any RDS survey, will be provided by the future systematic review, in accordance with this protocol, to assist researchers, policy makers, and service providers in improving RDS methods for the surveillance of any key population. CLINICALTRIAL PROSPERO CRD42022346470; https://tinyurl.com/54xe2s3k INTERNATIONAL REGISTERED REPORT DERR1-10.2196/43722
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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 | 0.088 | 0.163 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.089 | 0.014 |
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".