Conducting high-frequency data collection in low-resource settings: Lessons from a financial diary study among women engaged in sex work in Uganda
Bibliographic record
Abstract
Poverty and economic insecurity are driving forces in entering sex work among women in low resource areas. This increases their risk for HIV by influencing the decision-making process for high-risk behaviors. Few studies examine financial behaviors and capacities of women engaged in sex work (WESW). This paper describes the methodology used in a financial diary study aimed at characterizing women's spending patterns within a larger prevention intervention trial among WESW in Uganda. From June 2019 to March 2020, a subsample of 150 women randomized to the combination HIV prevention and economic empowerment treatment were asked to complete financial diaries to monitor daily expenditures in real time. Two hundred forty financial diaries were distributed to study participants during the financial literacy sessions at 8 sites. A total of 26,919 expense entries were recorded over 6 months. Sex work related expenses comprised approximately 20.01% of the total. The process of obtaining quality and consistent data was challenging due to the transient and stigmatized nature of sex work coupled with women's varying levels of education. Frequent check-ins, using peer support, code word or visuals, and a shorter timeframe would allow for a more accurate collection of high frequency data. Moreover, the ability of women to complete the financial diaries despite numerous challenges speaks to their potential value as a data collection tool, and also as an organizing tool for finances.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".