Quality improvement in public–private partnerships in low- and middle-income countries: a systematic review
Bibliographic record
Abstract
BACKGROUND: Public-private partnerships (PPP) are often how health improvement programs are implemented in low-and-middle-income countries (LMICs). We therefore aimed to systematically review the literature about the aim and impacts of quality improvement (QI) approaches in PPP in LMICs. METHODS: We searched SCOPUS and grey literature for studies published before March 2022. One reviewer screened abstracts and full-text studies for inclusion. The study characteristics, setting, design, outcomes, and lessons learned were abstracted using a standard tool and reviewed in detail by a second author. RESULTS: We identified 9,457 citations, of which 144 met the inclusion criteria and underwent full-text abstraction. We identified five key themes for successful QI projects in LMICs: 1) leadership support and alignment with overarching priorities, 2) local ownership and engagement of frontline teams, 3) shared authentic learning across teams, 4) resilience in managing external challenges, and 5) robust data and data visualization to track progress. We found great heterogeneity in QI tools, study designs, participants, and outcome measures. Most studies had diffuse aims and poor descriptions of the intervention components and their follow-up. Few papers formally reported on actual deployment of private-sector capital, and either provided insufficient information or did not follow the formal PPP model, which involves capital investment for a explicit return on investment. Few studies discussed the response to their findings and the organizational willingness to change. CONCLUSIONS: Many of the same factors that impact the success of QI in healthcare in high-income countries are relevant for PPP in LMICs. Vague descriptions of the structure and financial arrangements of the PPPs, and the roles of public and private entities made it difficult to draw meaningful conclusions about the impacts of the organizational governance on the outcomes of QI programs in LMICs. While we found many articles in the published literature on PPP-funded QI partnerships in LMICs, there is a dire need for research that more clearly describes the intervention details, implementation challenges, contextual factors, leadership and organizational structures. These details are needed to better align incentives to support the kinds of collaboration needed for guiding accountability in advancing global health. More ownership and power needs to be shifted to local leaders and researchers to improve research equity and sustainability.
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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 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".