Using systems thinking to understand scale-up and sustainability of health innovation: a case study of Seasonal malaria chemoprevention processes in Burkina Faso
Bibliographic record
Abstract
Abstract Background: Scale-up and sustainability are often studied separately, with few studies examining the interdependencies between these two processes and the implementation contexts of malaria innovations. Researchers and implementers offer much more attention to the content of innovations, focusing on the technological dimensions and the conditions for expansion. Researchers have often considered innovation a linear sequence in which scaling up and sustainability remained the last stages. Using systems thinking in this manuscript, we aim to analyze the complex scaling and sustainability processes through adopting and implementing Seasonal Malaria Chemoprevention (SMC) in Burkina Faso from 2014 to 2018. Methods: We conducted a qualitative case study involving 141 retrospective secondary data (administrative, press, scientific, tools and registries, and verbatim), spanning 2012 to 2018. We completed these with primary data collected between February to March 2018, as 15 personal semi-structured interviews with SMC's stakeholders and non-participant observations. Processual analysis permitted us to conceptualize scale-up and sustainability processes over time according to different vertical and horizontal levels of analysis and their interconnections. Results: Our results indicated six internal and external determinants of SMC that may negatively or positively influence its scale-up and sustainability in time and space. These determinants are effectiveness; monitoring and evaluation systems; resources (financial, material, and human); leadership and governance; adaptation to the local context; and other external elements. Our results revealed that donors and implementing actors prioritized financial resources over other determinants. In contrast, our study clearly showed that the sustainability of the innovation, as well as its scaling up, depends significantly on the consideration of the interconnectedness of the determinants. Each determinant can concurrently constitute an opportunity and a challenge for the success of the innovation. Conclusion: Our findings highlight the usefulness of the systemic perspective to consider all contexts (international, national, subnational, and local) to achieve large-scale improvement in the quality, equity, and effectiveness of interventions in global health. Thus, complex and systems thinking has made it possible to observe emergent and dynamic innovation behaviors and the dynamics particular to sustainability and scaling up processes.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".