Impact of a Matched Savings Program on Survivors of Human Trafficking and Gender-Based Violence in the Philippines/ Eşleştirilmiş Bir Tasarruf Programının Filipinler'de İnsan Ticareti ve Toplumsal Cinsiyete Dayalı Şiddet Mağdurları Üzerindeki Etkisi
Bibliographic record
Abstract
Survivors of human trafficking commonly experience significant financial difficulties, including lack of access to secure employment, recurring debt, minimal savings, and pressures to provide financially for their families. These experiences can exacerbate their vulnerability to experiencing further violence. Although economic empowerment interventions are greatly needed for this population, few evaluations have been conducted of such programs. In this manuscript, we present findings from an assessment of the Barug program, a two-year matched savings and financial literacy program for survivors of human trafficking and gender-based violence and their family members in the Philippines. Quantitative and qualitative data were collected from 10 survivor graduates of the Barug program through a combination of structured surveys, in-depth interviews, and focus group discussions (FGDs). Quantitative findings demonstrated increases in participants’ savings after completion of the Barug program. Thematic analysis revealed five themes regarding survivors’ experiences in the program: prioritizing asset development, enhanced budgeting skills, escaping a cycle of debt, psychosocial impact, and improved family relationships. Survivors described the benefits of Barug as multi-faceted, including financial, social, and psychological effects. Findings reveal the promise of an integrated matched savings, financial literacy, and psychosocial support program in helping survivors achieve greater financial stability and psychosocial wellbeing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".