10.B. Workshop: The COVID-19 Health Systems Response Monitor: what can we learn for the future
Bibliographic record
Abstract
Abstract The Health Systems Response Monitor (HSRM) was established in March 2020 at the beginning of the COVID-19 pandemic to collect and organize up-to-date information on how 50 countries in the WHO European Region and Canada are responding to the crisis. The HSRM uses a structured template, focusing primarily on the responses of health systems but also capturing wider public health initiatives. The HSRM is a joint undertaking of the WHO Regional Office for Europe, the European Observatory on Health Systems and Policies, and the European Commission. The HSRM builds on the Health Systems and Policy Network of the European Observatory, which brings together an international group of high-profile institutions from Europe and beyond with high academic standing in health systems and policy analysis. The content collected on the HSRM platform has been used to enhance cross country learnings through topical snapshots, papers, webinars and more. At the end of 2020, the European Observatory initiated a Health Policy Special Issue (SI) based on the work of the HSRM containing both thematic policy papers and full comparative descriptions and analysis of country responses. Overall, work from the HSRM aims to provide coherent and comprehensive insight on lessons learned from the COVID-19 response so far to support policymakers for future waves or in case of future pandemics. This workshop aims to provide the audience with an overview of the COVID-19 HSRM and the insights gleaned from the SI. An introductory overview will briefly introduce the COVID-19 HSRM and the SI. The first presentation will compare provider payment adjustments across 20 countries. The second presentation will focus on how countries planned services for potential surge capacity, managed care provision, and maintained routine services in both hospital and outpatient settings during the first wave of the pandemic. The third presentation will zoom in on experiences in primary health care. The last presentation will feature one of the country comparison papers from social health insurance countries. The workshop will conclude with an audience discussion about the lessons learned from the COVID-19 HSRM, and future opportunities for research using the HSRM. Key messages The COVID-19 Health System Response Monitor has tracked the progression of health policies throughout the pandemic and captures particularly policy relevant aspects. Both thematic and cross-country comparisons can illuminate how health systems have responded to the COVID-19 pandemic and provide lessons for future waves and pandemics.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.086 | 0.055 |
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".