Understanding health innovation adoption: A realist evaluation of pulse oximeter implementation in primary care for children under five in four West African countries
Bibliographic record
Abstract
Introduction Hypoxemia is an important contributor to child mortality, particularly in low-resource settings where diagnostic tools are scarce. The AIRE project introduced pulse oximeters (POs) into 202 primary healthcare centres (PHCs) in Burkina Faso, Guinea, Mali, and Niger, integrating them into the Integrated Management of Childhood Illness (IMCI) guidelines. This initiative aimed to strengthen diagnostic capacities for identifying hypoxemia and to improve care management for critically ill children under five. This study examined how healthcare workers (HCWs) adopted POs and explored the contexts and mechanisms influencing their adoption. Methods We conducted a realist evaluation to analyse adoption patterns, focusing on interactions between the Intervention, Contexts, Actors, Mechanisms, and Outcomes (ICAMO configurations). Data collection included 299 interviews with HCWs, patients' families, and institutional actors, conducted in 16 selected PHCs, at the institutional level, and in district hospitals, complemented by site observations. Analysis was performed using NVivo software, identifying ICAMO configurations as demi-regularities to explain variations in PO use and adoption. Results Training enabled HCWs to recognize the utility of POs, further motivating their use. Support-focused supervision fostering a sense of support, while control-focused approaches sometimes resulted in mechanical use driven by external pressure. In contexts of high workloads and children's agitation, difficulties in using POs were observed. In settings with limited diagnostic tools, POs increased HCWs' diagnostic confidence, encouraging adoption and improving decision-making. Observing or knowing the benefits of POs on children's health provided HCWs with a sense of relief and pride, further reinforcing PO adoption. However, structural barriers and challenges related to institutional adoption may threaten long-term use. Conclusion This study highlights the necessary conditions for adopting POs in PHCs. While widely used by healthcare workers, addressing challenges related to training, supply chain logistics, and referral systems to hospitals is essential to ensure long-term sustainability and improve child health outcomes.
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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 | 0.040 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 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".