Conducting Violence and Mental Health Research with Female Sex Workers during the COVID-19 Pandemic: Ethical Considerations, Challenges, and Lessons Learned from the Maisha Fiti Study in Nairobi, Kenya
Bibliographic record
Abstract
Conducting violence and mental health research during the COVID-19 pandemic with vulnerable groups such as female sex workers (FSWs) required care to ensure that participants and the research team were not harmed. Potential risks and harm avoidance needed to be considered as well as ensuring data reliability. In March 2020, COVID-19 restrictions were imposed in Kenya during follow-up data collection for the Maisha Fiti study (n = 1003); hence data collection was paused. In June 2020, the study clinic was re-opened after consultations with violence and mental health experts and the FSW community. Between June 2020 and January 2021, data were collected in person and remotely following ethical procedures. A total of 885/1003 (88.2%) FSWs participated in the follow-up behavioural–biological survey and 47/47 (100%) participated in the qualitative in-depth interviews. A total of 26/885 (2.9%) quantitative surveys and 3/47 (6.4%) qualitative interviews were conducted remotely. Researching sensitive topics like sex work, violence, and mental health must guarantee study participants’ safety and privacy. Collecting data at the height of COVID-19 was crucial in understanding the relationships between the COVID-19 pandemic, violence against women, and mental health. Relationships established with study participants during the baseline survey—before the pandemic—enabled us to complete data collection. In this paper, we discuss key issues involved in undertaking violence and mental health research with a vulnerable population such as FSWs during a pandemic. Lessons learned could be useful to others researching sensitive topics such as violence and mental health with vulnerable populations.
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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 | 0.333 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| 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".