FERTILITY CARE IN LOW- AND MIDDLE-INCOME COUNTRIES: Policy, politics, and macro-level influences on implementation in Uganda
Bibliographic record
Abstract
Graphical Abstract Abstract Background Infertility is a reproductive disease affecting millions globally. In Sub-Saharan Africa, the burden is considerably higher, affecting one in four couples. The psychosocial and economic impacts of infertility remain severe. Furthermore, restricted access to affordable fertility services is justified by international population reduction agendas and limited resources, resulting in inequitable access. Treatment, when available, is primarily through private sector clinics at catastrophically high costs. For this reason, low-cost IVF (LCIVF) technologies have been developed to simplify and minimize treatment costs. Still, there are limited studies on their adoption and utilization in the region. Methods A qualitative case study was used to explore implementation of LCIVF technologies in Uganda’s public health system. Macro-level factors influencing implementation of an ART department at Mulago Women’s Hospital were assessed through semi-structured interviews conducted with 21 actors, along with hospital observations, field notes, and document review. A combination of inductive and deductive thematic analysis techniques was used for data analysis in NVivo 12, guided by the Consolidated Framework for Implementation Research (CFIR). Results Following our analysis, several factors facilitated macro-level implementation including acknowledgment of infertility as a reproductive disease, strong political advocacy and oversight, government funding, and multi-organizational collaboration. Barriers included poor public knowledge, absence of legislation, limited community leader engagement, and diminished political support. Contributions This study contributed to knowledge on external factors that influence sustainable implementation of LCIVF initiatives in low-resource settings and is one of the first studies to apply CFIR to fertility care implementation in a low-resource setting. Lay summary Infertility is a reproductive disease that makes it difficult to conceive a child. Globally, infertility impacts millions of people. In Sub-Saharan Africa, the rate of infertility is surprisingly higher. There are few centers providing affordable treatment. This is partly because population reduction is an international priority, neglecting many who cannot have even one child. Cheaper treatment options have been developed to make treatment more accessible and affordable. This study looked at how international and national politics influenced inclusion and access to fertility care in an African context. A qualitative case study design approach was used. Our analysis found international acceptance of infertility as a disease, strong political support, and financial support aided inclusion. Still, limited public knowledge, engagement of community leaders, and diminished political support slowed down efforts. This research suggests that international agendas, political support, and community engagement are needed for sustained inclusion of fertility services in low-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".