Building sustainable and scalable peer-based programming: promising approaches from TESFA in Ethiopia
Bibliographic record
Abstract
Abstract Background Girls in Ethiopia’s Amhara region experience high rates of child marriage and are less able to negotiate sex or use family planning. Seeking to improve their lives, CARE’s TESFA programme delivered reproductive health and financial savings curricula to married girls via reflective dialogues in peer-based solidarity groups. From 2010 to 2013, 5,000 adolescent girls participated via three intervention arms: sexual and reproductive health, economic empowerment, and a combination of both. At end-line, participants reported improvements across health and empowerment outcomes. Four years post-TESFA, 88% of groups reported meeting without continued assistance from CARE. Some original participants had created new groups based on the TESFA model, and some girls not recruited for TESFA spontaneously replicated it to create their own groups. However, questions remained about what had contributed to this organic sustainment and scale-up of groups. Methods This 2018 study investigated factors affecting sustainability and scale-up of peer solidarity groups through a systematic mapping of TESFA groups across five woredas (districts) and interviews with key stakeholders. Data were collected from 39 focus groups with active and dissolved Girl Groups, Social Analysis and Action groups, and girls’ husbands and from 29 in-depth interviews with group facilitators and community health workers across three districts. Data were coded and analyzed per grounded theory principles. Results Changes in reproductive health knowledge and specific behaviours, such as contraceptive use and institutional delivery, were maintained 5 years after the intervention ended. Group connectedness, spousal support, integration of holistic community platforms, and opportunities for financial independence were found to be important for group sustainability. Observed changes in TESFA girls’ confidence to negotiate and assert their rights, hopes of improved mobility, and the promise of economic opportunity commonly inspired spontaneous replication of groups. Recommendations for future peer-based programmes include creating environments of solidarity and holistically engaging intervention communities. Conclusion By increasing knowledge of and access to reproductive health services, TESFA mitigates some of the harmful effects of child marriage. The maintenance and organic replication of groups suggest that TESFA provides a successful, scalable and sustainable tested model for reproductive health program delivery through peer-based solidarity groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.683 | 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 teacher head, 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".