That’s Where Our Humanness Lies”: How Families Leverage Social Capital to Access Antenatal Care in Rural, Eastern Zimbabwe
Bibliographic record
Abstract
Families across sub-Saharan Africa (SSA) often rely on social capital to access traditional, faith-based, and biomedical antenatal care (ANC). Yet, many current studies focus on biomedical ANC and emanate predominantly from the Global North. In this qualitative case study, we aimed to explore how families utilize social capital to access various types of ANC in Mafararikwa, a rural ward in Eastern Zimbabwe. We generated data through interviews with 30 health professionals, and consultations, focus group discussions, and storyboarding with 71 community-level key informants and parents or legal guardians. We analyzed the data thematically. We identified four main themes: Theme 1: Support through social networks and community groups (families draw support from social networks, local groups, and community initiatives to engage with different types of ANC); Theme 2: Material and non-material support (families draw material and non-material supports from social networks, local groups, and community initiatives to engage with various forms of ANC); Theme 3: Reciprocal norms and sociocultural practices (families leverage reciprocal actions, norms of respect and courtesy towards pregnant women, mutual trust, and socio-cultural practices to engage with diverse ANC types); and Theme 4: Informal supports and care mixing dynamics (local informal supports shape families’ interactions with different types of ANC). The complex ways families in Mafararikwa leverage social capital to access preferred forms of ANC reflect context-specific relationships and holistic health conceptions grounded in Ubuntu . Culturally-specific responses that strengthen these relationships are needed to ensure equitable ANC outcomes in Mafararikwa and beyond.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".