Barriers and opportunities to disseminate and translate evidence from implementation research and quality improvement in the context of resource limited settings
Bibliographic record
Abstract
The use of research evidence to influence policy decisions and practice is crucial for improving health service delivery and outcomes. Effective influence requires timely dissemination and translation of evidence to practitioners and policymakers at all decision-making levels. Traditional literature on research dissemination strategies has primarily focused on the Global North, often reflecting funding structures from that region. Yet there is a wealth of research, especially operational research as well as quality improvement (QI) initiatives that have been central to programs in the Global South where health resources are often limited. This article examines the challenges and opportunities of turning research and quality improvement work into policy and practice in resource-limited settings. It draws on insights from the authors’ roles as panelists and moderators at the 3rd Annual Research and Quality Improvement Symposium, hosted by Partners In Health and the Ministry of Health of Sierra Leone on November 19, 2024. Barriers to effective dissemination of scientific evidence include limited collaboration among partners, funding constraints, visa issues and travel restrictions. Additionally, inequities in research authorship, gender disparities and language barriers hinder evidence dissemination. Building strong collaborations between policymakers and researchers presents valuable opportunities to bridge these gaps. Innovative methods such as digital platforms can also enhance dissemination despite resource limitations. Further, decolonizing global health research and ensuring equitable access to resources are critical for effective evidence sharing. Advocating for local conferences and leveraging funding mechanisms from the Global South can further support researchers in these contexts. To enhance healthcare quality in resource-limited settings, innovative strategies for sharing and implementing research and quality improvement initiatives are essential. Local researchers can utilize local knowledge, technology and partnerships to effectively disseminate findings that meet community needs. Strategic communication and collaboration among stakeholders are vital for translating research into policies that positively impact 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 | 0.386 | 0.532 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".