User-Centered Design for Designing and Evaluating a Prototype of a Data Collection Tool to Submit Information About Incidents of Violence Against Sex Workers: Multiple Methods Approach
Bibliographic record
Abstract
Background: Sex workers face an epidemic of violence in the United States. However, violence against sex workers in the United States is underreported. Sex workers hesitate to report it to the police because they are frequently punished themselves; therefore, an alternative for reporting is needed. Objective: We aim to apply human-centered design methods to create and evaluate the usability of the prototype interface for ReportVASW (violence against sex worker, VASW) and identify opportunities for improvement. Methods: This study explores ways to improve the prototype of ReportVASW, with particular attention to ways to improve the data collection tool. Evaluation methods included cognitive walkthrough, system usability scale, and heuristic evaluation. Results: End users were enthusiastic about the idea of a website to document violence against sex workers. ReportVASW scored 90 on the system usability scale. The tool scored neutral on consistency, and all other responses were positive toward the app, with most being strong. Conclusions: Many opportunities to improve the interface were identified. Multiple methods identified multiple issues to address. Most changes are not overly complex, and the majority were aesthetic or minor. Further development of the ReportVASW data collection tool is worth pursuing.
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 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 | 0.099 | 0.100 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".