Factors influencing antenatal care attendance for Bakiga and Indigenous Batwa women in Kanungu District, Southwestern Uganda
Bibliographic record
Abstract
INTRODUCTION: The number of maternal deaths remains high in sub-Saharan Africa. Effective antenatal care (ANC) reduces maternal morbidity and mortality; therefore, provision of improved antenatal care services has been prioritised across sub-Saharan Africa. Yet, research is limited on the ANC experiences of Indigenous women in sub-Saharan Africa. This study characterised ANC attendance patterns at a hospital, and characterised factors influencing attendance among Bakiga and Indigenous Batwa women in Kanungu District, Uganda. METHODS: A community-based, mixed-methods approach was used. Quantitative data were collected from a local hospital (records for 2299 ANC visits) and analysed by using descriptive statistics and multivariable regression analysis. Qualitative data from eight key informant interviews (n=9 healthcare providers) and 16 focus group discussions (n=120 Batwa and Bakiga women) were analysed by thematic analysis. RESULTS: Most ANC patients attended between one and three ANC visits per pregnancy (n=1259; 92.57%), and few attended the recommended four or more visits (n=101; 7.43%). Distance from a woman's home to the hospital was significantly associated with lower ANC attendance (p<0.05, 95% confidence interval 0.01-0.96), after adjusting for maternal age and number of previous pregnancies. The qualitative data revealed that many factors influenced ANC attendance for both Batwa and Bakiga: long distances from the home to a health centre, high direct and indirect costs of ANC, lack of power in household decision-making, and poor interactions with healthcare providers. While the types of barriers were similar among Batwa and Bakiga, some were more pronounced for Indigenous Batwa women. CONCLUSION: This study partnered with and collected in-depth data with Indigenous Peoples who remain underrepresented in the literature. The findings indicated that Indigenous Batwa continue to face unique and more pronounced barriers to accessing ANC in Kanungu District, Uganda. Ensuring access to ANC for these populations requires an in-depth understanding of their experiences within the local healthcare context. To reduce health inequities that Indigenous Peoples experience, policy-makers and healthcare workers need to adequately understand, effectively address, and appropriately prioritise factors influencing ANC attendance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".