Co-designing a digital health tool to deliver occupational health and safety strategies for sex workers
Bibliographic record
Abstract
Abstract Background: For the past twenty-five years, sex workers have been using Information and Communication Technologies, such as the Internet, websites, emails, blogs, texting, mobile phones, and social media, to receive and exchange occupational health and safety information. However, previous research has indicated that sex workers would prefer to use a sex worker-only digital occupational health and safety tool, designed by sex workers for sex workers. By forming an alliance with technologists and researchers, sex workers can design a digital platform that can meet their occupational health and safety needs by using a collaborative and participatory approach known as co-design. Objective: In applying the practice of co-design to create a digital prototype, we sought to answer the following research question “What are the core components of a digital tool that will enable the delivery of occupational health and safety strategies for sex workers?” Methods: Using the Auckland District Health Board of New Zealand's Health Service Co-design framework, three co-design sessions were held. Due to the COVID-19 pandemic, the three co-design sessions for this study were held virtually. Results: A total of 6 sex workers from 4 Eastern Canadian cities participated in 3 co-design sessions. During the first session, journey mapping, participants sketched out their stories by discussing their experiences using various digital tools (e.g., group chats) to exchange safety tips. In the second session, participants used outputs from the first session to further discuss their needs and develop scenarios, sketching the steps they would take to access a digital occupational health and safety resource. During the third session, participants used outputs from the prior sessions to formulate the core components of the digital tool. The resulting prototype, a website participants named SWanswers (Sex Work answers), was comprised of 6 core components: regional bad date resources, the work of sex work, supplies, STI information and education, sexual health, and harm reduction services. Conclusion: Findings from the co-design approach employed in this study could help guide sex workers, academics, and technologists when collaborating on digital health endeavours.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".