Why don’t illiterate women in rural, Northern Tanzania, access maternal healthcare?
Bibliographic record
Abstract
BACKGROUND: In 2017, roughly 540 women in Sub-Saharan Africa died every day from preventable causes related to pregnancy and childbirth. To stem this public-health crisis, the WHO recommends a standard continuity of maternal healthcare, yet most women do not receive this care. Surveys suggest that illiteracy limits the uptake of the recommended care, yet little is understood about why this is so. This gap in understanding why healthcare is not sought by illiterate women compromises the ability of public health experts and healthcare providers to provide culturally relevant policy and practice. This study consequently explores the lived experiences related to care-seeking by illiterate women of reproductive age in rural Tanzania to determine why they may not access maternal healthcare services. METHODS: An exploratory, qualitative study was conducted in four communities encompassing eight focus group discussions with 81 illiterate women, 13 in-depth interviews with illiterate women and seven key-informant interviews with members of these communities who have first-hand experience with the decisions made by women concerning maternal care. Interviews were conducted in the informant's native language. The interviews were coded, then triangulated. RESULTS: Two themes emerged from the analysis: 1) a communication gap arising from a) the women's inability to read public-health documents provided by health facilities, and b) healthcare providers speaking a language, Swahili, that these women do not understand, and 2) a dependency by these women on family and neighbors to negotiate these barriers. Notably, these women understood of the potential benefits of maternal healthcare. CONCLUSIONS: These women knew they should receive maternal healthcare but could neither read the public-health messaging provided by the clinics nor understand the language of the healthcare providers. More health needs of this group could be met by developing a protocol for healthcare providers to determine who is illiterate, providing translation services for those unable to speak Swahili, and graphic public health messaging that does not require literacy. A failure to address the needs of this at-risk group will likely mean that they will continue to experience barriers to obtaining maternal care with detrimental health outcomes for both mothers and newborns.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".