ArticleHealth policy and planning2024
From PERFORM to PERFORM2Scale: lessons from scaling-up a health management strengthening intervention to support Universal Health Coverage in three African countries.
Article in Health policy and planning, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- The Relevance, Delivery, and Effects of Health Management, Leadership, and Governance Training on Managers' Practices and Organizational Results: A Protocol for an Effectiveness-Implementation Hybrid Type 2 Study.Health science reports · 2026Article
- When donor funding ends: reflections from Jordan's Health Care Accreditation Council on sustainable health system strengthening.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2026Article
- Learning sites for health systems research: Reflections on five programs in Africa, Asia, and Central America.Learning health systems · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Strengthening management and leadership competencies among district and local health managers has emerged as a common approach for health systems strengthening and to achieve Universal Health Coverage (UHC). While the literature is rich with localized examples of initiatives that aim to strengthen the capacity of district or local health managers, particularly in sub-Saharan Africa, considerably less attention is paid to the science of 'how' to scale-up these initiatives. The aim of this paper is thus to examine the 'process' of scaling-up a management strengthening intervention (MSI) and identify new knowledge and key lessons learned that can be used to inform the scale-up process of other complex health interventions, in support of UHC. Qualitative methods were used to identify lessons learned from scaling-up the MSI in Ghana, Malawi and Uganda. We conducted 14 interviews with district health management team (DHMT) members, three scale-up assessments with 20 scale-up stakeholders, and three reflection discussions with 11 research team members. We also kept records of activities throughout MSI and scale-up implementation. Data were recorded, transcribed and analysed against the Theory of Change to identify both scale-up outcomes and the factors affecting these outcomes. The MSI was ultimately scaled-up across 27 districts. Repeated MSI cycles over time were found to foster greater feelings of autonomy among DHMTs to address longstanding local problems, a more innovative use of existing resources without relying on additional funding and improved teamwork. The use of 'resource teams' and the emergence of MSI 'champions' were instrumental in supporting scale-up efforts. Challenges to the sustainability of the MSI include limited government buy-in and lack of sustained financial investment.
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.