Methodological and Ethical Implications of Using Remote Data Collection Tools to Measure Sexual and Reproductive Health and Gender-Based Violence Outcomes among Women and Girls in Humanitarian and Fragile Settings: A Mixed Methods Systematic Review of Peer-Reviewed Research
Bibliographic record
Abstract
Purpose: This systematic review investigates the methodological and ethical implications of using remote data collection tools to measure sexual/reproductive health (SRH) and gender-based violence (GBV) outcomes among women and girls in humanitarian and fragile settings. Methods: We included empirical studies of all design types that collected any self-reported primary data related to SRH/GBV using information and communication technology, in the absence of in-person interactions, from women and girls in humanitarian and fragile settings. The search was run in March 2021 without filters or limits in Ovid Medline, Embase, Web of Science, Clinicaltrials.gov , and Scopus. Quality was assessed using an adapted version of the MMAT tool. Two reviewers independently determined whether each full text source met the eligibility criteria, and conflicts were resolved through consensus. A-priori extraction fields concerned methodological rigor and ethical considerations. Results: 21 total studies were included. The majority of studies were quantitative descriptive, aiming to ascertain prevalence. Telephone interviews, online surveys, and mobile applications, SMS surveys, and online discussion forums were used as remote data collection tools. Key methodological considerations included the overuse of non-probability samples, lack of a defined sampling frame, the introduction of bias by making eligibility contingent on owning/accessing technology, and the lack of qualitative probing. Ethical consideration pertained to including persons with low literacy, participant safety, use of referral services, and the gender digital divide. Conclusion: Findings are intended to guide SRH/GBV researchers and academics in critically assessing methodological and ethical implications of using remote data collection tools to measure SRH and GBV in humanitarian and fragile settings.
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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 | 0.435 | 0.684 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".