Designing and Evaluating a Prototype of a Trilingual Data Collection Tool for the Middle East and North Africa (MENA) Region to Collect Data About Violence Against Sex Workers: Multiple Methods Approach in User-Centered Design
Bibliographic record
Abstract
Background: Sex workers face an epidemic of violence around the world. However, violence against sex workers (VASW) is underreported, and sex workers hesitate to report to the police because they are frequently punished; therefore, an alternative for reporting is needed. Sex workers also face stigmatization from health care workers, further discouraging help-seeking behavior or reporting in health care facilities. Sex workers have been recipients of services from nongovernmental organizations, typically related to HIV and sexual transmission of infections, but violence remains underaddressed. Objective: This study aims to apply human-centered design methods to adapt ReportVASW for use in the Middle East and North Africa and to evaluate the usability of the prototype interface and identify opportunities for improvement. Methods: Evaluation methods included cognitive walkthrough and System Usability Scale by 9 potential end users, and heuristic evaluation by 2 informatics professionals and 2 service providers. Results: This study explores ways to improve the trilingual prototype of ReportVASW, with particular attention to ways to improve the data collection tool. Multiple methods identified multiple issues to address. Heuristic analysis revealed 2 serious issues to address, with scores over 2.5 out of 4 in the tool used. The most serious problems identified in heuristic analysis were related to language, particularly the Arabic version. Translation issues were addressed before end user testing. End users were enthusiastic about the idea of a mobile tool to document VASW, provided it led to change. They gave ReportVASW a System Usability Scale of 91.4, above the 68 considered good. Even as end users were enthusiastic, they offered suggestions for improvement. Conclusions: Many opportunities to improve the interface were identified. Most changes are not overly complex, and the majority involve adapting the language used and improving the translation. Development of the trilingual ReportVASW data collection tool for the Middle East and North Africa region is worth pursuing.
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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 | 0.038 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".