Developing a Pictorial Sisterhood Method in collaboration with illiterate Maasai traditional birth attendants in northern Tanzania
Bibliographic record
Abstract
OBJECTIVE: To study whether data on maternal mortality can be gathered while maintaining local ownership of data in a pastoralist setting where a scarcity of data sources and a culture of silence around maternal death amplifies limited awareness of the magnitude of maternal mortality. METHODS: As part of a participatory action research project, investigators and illiterate traditional birth attendants (TBAs) collaboratively developed a quantitative participatory tool-the Pictorial Sisterhood Method-that was pilot-tested between March 12 and May 30, 2011, by researchers and TBAs in a cross-sectional study. RESULTS: Fourteen TBAs interviewed 496 women (sample), which led to 2241 sister units of risk and a maternal mortality ratio of 689 deaths per 100000 live births (95% confidence interval 419-959). Researchers interviewed 474 women (sample), leading to 1487 sister units of risk and a maternal mortality ratio of 484 (95% confidence interval 172-795). CONCLUSION: The Pictorial Sisterhood Method is an innovative application that might increase the participation of illiterate individuals in maternal health research and advocacy. It offers interesting opportunities to increase maternal mortality data ownership and awareness, and warrants further study and validation.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".