Accumulation of Biological and Behavioral Data of Female Sex Workers Using Respondent-Driven Sampling: Protocol for a Systematic Review
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. TRIAL REGISTRATION: PROSPERO CRD42022346470; https://tinyurl.com/54xe2s3k. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43722.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| grok | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| opus | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".