Setting health systems research priorities for Afghanistan: an application of the child health and nutrition research initiative (CHNRI) methodology to set a roadmap to 2030
Bibliographic record
Abstract
INTRODUCTION: Afghanistan's health system has faced considerable challenges since the Taliban takeover in 2021, leaving the population vulnerable to an increased risk of morbidity and mortality. Research to illuminate the current functioning of the health system and approaches for strengthening its key components is critically needed to address imminent and evolving health needs of the Afghan people. METHODS: approach has yet to be developed. Using the Child Health and Nutrition Research Initiative methodology, this study identifies the top 20 health systems' research priorities among experienced Afghanistan health researchers. Priorities were also considered when disaggregating data by subgroups, such as Afghan versus non-Afghan respondents and those from low- and middle-income versus high-income settings. RESULTS: A total of 303 researchers were invited to score the research questions; 86 responded to the scoring survey and 55 completed it (60% were of Afghan origin). The highest priority questions were relatively diverse in terms of topic area, with questions spanning system-level factors, healthcare quality, community-based healthcare, improvements in the pharmaceutical sector, epidemiological trends, health management information systems and surveillance, access to care and approaches to improving service delivery in Afghanistan, among many others. 'Delivery'-focused and 'development'-focused questions were prioritised, demonstrating that participants assigned greater importance to more practical research questions that would explore features of and approaches to improving existing health system structures within the current Afghan context. Results were consistent across subgroups. CONCLUSION: This research prioritisation exercise fills a gap by generating consensus and establishing a research agenda for strengthening Afghanistan's health system.
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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 | 0.131 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".