ArticleLearning health systems2024
A conceptual learning analysis of paired after action and intra action reviews for health emergencies.
Article in Learning health systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Resurgence of malaria in Ethiopia: a national landscape analysis of systemic and operational gaps.Malaria journal · 2026Article
- Assessing the policy and institutional framework for multisectoral governance and accountability in emergency preparedness and response in Ethiopia.PLOS global public health · 2026Article
- A conceptual learning analysis of paired after action and intra action reviews for health emergencies.Learning health systems · 2024Article
- Envisioning public health as a learning health system.Learning health systems · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Processes of self-reflection and the learning they allow are crucial before, during, and after acute emergencies, including infectious disease outbreaks. Tools-such as Action Reviews-offer World Health Organization (WHO) member states a platform to enhance learning. We sought to better understand the value of these tools and how they may be further refined and better used. Methods: We searched the publicly available WHO Strategic Partnership for Health Security website for paired reports of Action Reviews, that is, reports with a comparable follow-up report. We complemented the paired action reviews, with a literature search, including the gray literature. The paired action reviews were analyzed using the "Learning Health Systems" framework. Results: We identified three paired action reviews: Lassa Fever After Action Reviews (AARs) in Nigeria (2017 and 2018), COVID-19 Intra-Action Reviews (IARs) in Botswana (2020 and 2021), and COVID-19 IARs in South Sudan (2020 and 2021). Action Reviews allowed for surfacing relevant knowledge and, by engaging the right (in different contexts) actors, asking "are we doing things right?" (single loop learning) was evident in all the reports. Single loop learning is often embedded within examples of double loop learning ("are we doing the right things?"), providing a more transformative basis for policy change. Triple loop learning ("are we learning right"?) was evident in AARs, and less in IARs. The range of participants involved, the level of concentrated focus on specific issues, the duration available for follow through, and the pressures on the health system to respond influenced the type (i.e., loop) and the effectiveness of learning. Conclusion: Action Reviews, by design, surface knowledge. With favorable contextual conditions, this knowledge can then be applied and lead to corrective and innovative actions to improve health system performance, and in exceptional cases, continuous learning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.