Research priorities to reduce the impact of COVID-19 in low- and middle-income countries
Bibliographic record
Abstract
Background: The COVID-19 pandemic has caused disruptions to the functioning of societies and their health systems. Prior to the pandemic, health systems in low- and middle-income countries (LMIC) were particularly stretched and vulnerable. The International Society of Global Health (ISoGH) sought to systematically identify priorities for health research that would have the potential to reduce the impact of the COVID-19 pandemic in LMICs. Methods: The Child Health and Nutrition Research Initiative (CHNRI) method was used to identify COVID-19-related research priorities. All ISoGH members were invited to participate. Seventy-nine experts in clinical, translational, and population research contributed 192 research questions for consideration. Fifty-two experts then scored those questions based on five pre-defined criteria that were selected for this exercise: 1) feasibility and answerability; 2) potential for burden reduction; 3) potential for a paradigm shift; 4) potential for translation and implementation; and 5) impact on equity. Results: Among the top 10 research priorities, research questions related to vaccination were prominent: health care system access barriers to equitable uptake of COVID-19 vaccination (ranked 1st), determinants of vaccine hesitancy (4th), development and evaluation of effective interventions to decrease vaccine hesitancy (5th), and vaccination impacts on vulnerable population/s (6th). Health care delivery questions also ranked highly, including: effective strategies to manage COVID-19 globally and in LMICs (2nd) and integrating health care for COVID-19 with other essential health services in LMICs (3rd). Additionally, the assessment of COVID-19 patients' needs in rural areas of LMICs was ranked 7th, and studying the leading socioeconomic determinants and consequences of the COVID-19 pandemic in LMICs using multi-faceted approaches was ranked 8th. The remaining questions in the top 10 were: clarifying paediatric case-fatality rates (CFR) in LMICs and identifying effective strategies for community engagement against COVID-19 in different LMIC contexts. Interpretation: Health policy and systems research to inform COVID-19 vaccine uptake and equitable access to care are urgently needed, especially for rural, vulnerable, and/or marginalised populations. This research should occur in parallel with studies that will identify approaches to minimise vaccine hesitancy and effectively integrate care for COVID-19 with other essential health services in LMICs. ISoGH calls on the funders of health research in LMICs to consider the urgency and priority of this research during the COVID-19 pandemic and support studies that could make a positive difference for the populations of LMICs.
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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 | 0.159 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".