From warehouse to ward: Applying implementation research methods to the device identification, qualification, distribution, and management process within the NEST360 alliance
Bibliographic record
Abstract
Abstract Background Preventing newborn deaths is possible with the right medical devices. However, in many countries, devices for small and sick newborn care (SSNC) are either unavailable, not fit for the environment, or broken. Newborn Essential Solutions and Technologies (NEST360) is a multicountry interdisciplinary alliance aimed at decreasing neonatal mortality in Kenya, Malawi, Nigeria, and Tanzania. The technology qualification, distribution and management teams in NEST360 work to ensure appropriate devices are available and functional at facilities. Their work involves identifying which technologies are needed, sourcing devices that fit those needs, testing those devices’ functional ability under varied conditions, establishing a reliable supply chain, training facility staff in use, and maintaining the devices once installed through trained biomedical technicians. We applied implementation research (IR) to understand context, describe strategy selection, and implementation outcomes of the work designed to ensure the consistent availability of functional devices for SSNC. Methods Between March and July 2024, we conducted in-depth interviews with NEST360 team members via Zoom, and reviewed quantitative programmatic data, including device functionality reports. We applied deductive content analysis for interviews and descriptive statistics for quantitative data. Results were used to develop an implementation research logic model (IRLM) using NEST360 and UNICEF’s SSNC Implementation Toolkit for contextual factors and RE-AIM for implementation outcomes. Results We identified 40 contextual factors, 78% being barriers. Twenty-one strategies were implemented to address barriers to device qualification, distribution, and management efforts, including engaging stakeholders and conducting ongoing trainings. Notable implementation outcomes included Reach with 29 devices in 12 product categories qualified, and all 66 facilities received NEST-qualified devices , Effectiveness , in 2024, an average of 87% of all newborn care devices were functional, including those provided by NEST360 and those sourced through existing channels, Adoption with over 2,476 devices installed at NEST360 sites in 2023 . Acceptability was also high with country-level biomedical technicians reporting positive facility-level experiences using the devices. Conclusions NEST360 approach to ensuring appropriate and functioning equipment for SSNC was successful through multiple strategies to address multilevel barriers. The use of IR facilitated understanding of how strategies addressed context and where change is needed. These results will be used in plans for scale-up and dissemination. Contributions to the literature This study utilizes implementation research (IR) methodologies to examine the contextual factors, strategies, and outcomes of maintaining functional medical devices for small and sick newborn care (SSNC) in four countries. By developing an implementation research logic model (IRLM), this study provides a structured approach to understanding how device access and sustainability can be improved in resource-limited settings. The study identified 40 contextual factors in multiple areas and levels influencing the availability of functional devices, including human resource constraints, governance issues, financial barriers, and infrastructure challenges. It maps these barriers and facilitators to 21 targeted strategies that align with the Expert Recommendations for Implementation Research (ERIC) framework, providing a blueprint for overcoming diverse challenges. The study explores long-term sustainability of the NEST360-supported work by identifying key areas for improvement, such as financing for spare parts, strengthening preventive maintenance practices, and advocating for policy reforms.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".