Assessing capacities and resilience of health services during the COVID-19 pandemic: Lessons learned from use of rapid key informant surveys
Bibliographic record
Abstract
This article is part of the Research Topic: 'Health Systems Recovery in the Context of COVID-19 and Protracted Conflict.' Problem: Many countries lacked rapid and nimble data systems to track health service capacities to respond to COVID-19. They struggled to assess and monitor rapidly evolving service disruptions, health workforce capacities, health products availability, community needs and perspectives, and mitigation responses to maintain essential health services. Method: Building on established methodologies, the World Health Organization developed a suite of methods and tools to support countries to rapidly fill data gaps and guide decision-making during COVID-19. The tools included: (1) a national "pulse" survey on service disruptions and bottlenecks; (2) a phone-based facility survey on frontline service capacities; and (3) a phone-based community survey on demand-side challenges and health needs. Use: Three national pulse surveys revealed persisting service disruptions throughout 2020-2021 (97 countries responded to all three rounds). Results guided mitigation strategies and operational plans at country level, and informed investments and delivery of essential supplies at global level. Facility and community surveys in 22 countries found similar disruptions and limited frontline service capacities at a more granular level. Findings informed key actions to improve service delivery and responsiveness from local to national levels. Lessons learned: The rapid key informant surveys provided a low-resource way to collect action-oriented health services data to inform response and recovery from local to global levels. The approach fostered country ownership, stronger data capacities, and integration into operational planning. The surveys are being evaluated to inform integration into country data systems to bolster routine health services monitoring and serve as health services alert functions for the future.
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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 | 0.139 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".