Nuestras Historias- Designing a novel digital story intervention through participatory methods to improve maternal and child health in the Peruvian Amazon
Bibliographic record
Abstract
BACKGROUND: In rural areas of the Loreto region within the Peruvian Amazon, maternal mortality rate is above the national average and the majority of women deliver at home without care from a trained health care provider. METHODS: To develop community-tailored videos that could be used for future interventions, we conducted Photovoice and digital storytelling workshops with community health workers (CHW) and mothers from 13 rural communities in the Parinari district. Through Photovoice we recognized local barriers to healthy pregnancies. Participants (n = 28) were trained in basic photography skills and ethics. They captured photos representing perceived pregnancy-related road-blocks and supports, and these photos identified central themes. Participants recorded personal stories and "storyboarded" to develop digital stories around these themes, and a Digital Story Curriculum called Nuestras Historias (Our Stories), was created. An acceptability survey of the digital stories was then conducted including 47 men (M) and 60 women (F). RESULTS: According to the PhotoVoice workshops, pregnancy-related problems included: lack of partner support, domestic violence, early pregnancies, difficulty attending prenatal appointments, and complications during pregnancy and delivery. Over 30 stories on these themes were recorded. Seven were selected based on clarity, thematic relevance, and narrative quality and were edited by a professional filmmaker. The acceptability survey showed that local participants found the digital stories novel (M = 89.4%, F = 83.3%), relatable (M = 89.4%, F = 93.2%), educational (M = 91.5%, F = 93.3%) and shareable (M = 100%, F = 100%). Over 90% of respondents rated the digital stories as "Excellent" or "Good", found the videos "Useful" and considered them "Relevant" to their communities. CONCLUSIONS: The digital stories address community-specific problems through narrative persuasion using local voices and photography. This combination had a high acceptability among the target population and can serve as a model for developing educational strategies in a community-tailored manner. This package of seven videos will be further evaluated through a cluster randomized trial.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".