Leaving no woman or girl behind? Inclusion and participation in digital maternal health programs in sub-Saharan Africa
Bibliographic record
Abstract
Across sub-Saharan Africa where access to adequate maternal healthcare is fraught with myriad challenges, especially for hard-to-reach populations, digital health technologies offer opportunities to improve maternal health outcomes. Digital health can circumvent inefficiencies in the traditional healthcare system and address challenges such as limited access to in-person medical consultations, and poor access to skilled birth attendants and health promotion activities. These benefits notwithstanding, digital health can be exclusionary. Too often, digital maternal health programs are not designed with a focus on equity in distribution nor are they designed from a gender equity standpoint. In this paper, we illustrate exclusionary practices of digital health programs through an extensive literature review of digital maternal health programs across sub-Saharan Africa. Taking an intersectional approach, we discuss how women are most vulnerable and excluded at the intersection of gender, literacy, and disability. Tackling exclusionary practices in digital health is crucial to ensure that no woman or girl is left behind.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".