Measuring what matters: key indicators for performance and resilience in fragile, low-income contexts. A scoping review
Bibliographic record
Abstract
BACKGROUND: Measuring and monitoring health system performance and resilience is key for planning and managing resilience-building activities. Recurrent simultaneous shocks, particularly in fragile contexts which are home to nearly one quarter of the world's population, underscore the need for resilient health systems able to provide the needed health care. This scoping review aims to examine how the performance and resilience of the health system have been assessed and measured in fragile, low-income contexts, identify gaps and provide recommendations to improve resilience measurement. METHODS: A scoping review of peer-reviewed literature on health systems' performance or resilience indicators was conducted following PRISMA guidelines. Only studies that were set in countries classified by the OECD as fragile and low-income and that took a whole-of-system approach were included. Of 2175 articles identified 18 met the inclusion criteria. Indicators were classified against the WHO's building blocks and four resilience dimensions, then assessed for comparability, feasibility and relevance to resilience. RESULTS: The studies covered 23 of the 24 countries classified as fragile and low-income by the OECD. A total of 466 indicators were identified. Among the four dimensions of resilience, 39% of the indicators assessed the system's capacity to manage multiple- and cross-scale dynamics and feedback (interdependence), and only 5% of the indicators assessed the capacity to anticipate and cope with shocks (uncertainty). Less than half of the indicators (n = 230) rely on data from routine health information systems or global datasets, those were classified as feasible. In total, 58% of indicators enable comparison across countries and subnational entities (comparable). Of the 218 indicators classified as both feasible to measure and comparable, 61 indicators were identified as relevant for assessing health system resilience and hence categorized as core indicators. CONCLUSIONS: Significant gaps remain in measuring health system resilience. Qualitative indicators lacked standardization, quantitative indicators did not track health system's status over time, and few assessed the capacity to anticipate shocks. Improving resilience measurement in fragile low-income contexts requires a whole-of-system approach, using indicators that assess the health system's capacity to respond to shocks, track performance over time, and provide feedback to guide policy decisions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.054 | 0.228 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.046 | 0.046 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".