A qualitative investigation of gender-based violence prevention and response using digital technologies in low resource settings and refugee populations
Bibliographic record
Abstract
Background: Governmental and non-governmental organizations across medical, legal, and psychosocial sectors providing care to survivors of gender-based violence (GBV) and their families rapidly digitalized services during the COVID-19 pandemic. GBV prevention/response services working with women and children who are forcibly displaced and/or living in low-and-middle income countries (LMIC) were no exception to the rapid digitalization trend. Literature is lacking a critical synthesis of best practices and lessons learned since digitalization replaced major operations involved in GBV prevention/response.Objective: This research qualitatively investigated how GBV service providers, located in a range of socio-political settings, navigated the process of digitalizing GBV prevention/response during the COVID-19 crisis.Method: Semi-structured key informant interviews (KII) with GBV service providers in varied sectors were implemented virtually (2020–2021) in Brazil, Guatemala, Iraq, and Italy (regarding forcibly displaced women/girls for the latter). Participants were recruited using purposive and snowball sampling. Interview guides covered a range of topics: perceived changes in violence and service provision, experiences with virtual services, system coordination, and challenges. The KIIs were conducted in Portuguese, Spanish, Arabic, and Italian. Interviews were audio-recorded, transcribed, and translated into English. The research team conducted thematic analysis within and between countries using a structured codebook of data driven and theory driven codes.Results: Major themes concerned the: (1) spectrum of services that were digitalized during the COVID-19 crisis; (2) gender digital divide as a barrier to equitable, safe, and effective service digitalization; (3) digital violence as an unintended consequence of increased digitalization across social/public services.Conclusion: Digitalization is a balancing act with respect to (1) the variety of remotely-delivered services that are possible and (2) the access/safety considerations related to the gender digital divide and digital violence.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".