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Record W4403816709 · doi:10.1093/eurpub/ckae144.105

2.L. Round table: Monitoring health reforms to inform policy innovation: the Health Systems and Policies Monitor

2024· article· en· W4403816709 on OpenAlexaboutno aff

Bibliographic record

VenueEuropean Journal of Public Health · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsTable (database)Round tableHealth policyBusinessPolitical scienceMedicineComputer scienceNursingPublic healthData mining

Abstract

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Abstract Background The European Observatory on Health Systems and Policies’ (Observatory) Health Systems and Policy Monitor (HSPM) network convenes an international group of around 50 national health systems researchers from over 40 institutions across 31 high-income countries, including all EU countries, Canada, Israel, the UK and the US. In advance of the HSPM annual meeting, experts share the current year’s “top-three” health reforms in their countries via a standardized survey, soliciting details on content and general implementation processes. Reforms are iteratively organized into to 2 of 11 clusters, derived from the WHO health system building blocks. Experts then discuss background, status and content of national reforms in the year’s main clusters and initiate cross-country collaborations. A repository of over 650 reforms since 2018 has been created and is available online, showcasing reform patterns in HSPM countries. The collected data have informed studies on reform trends in HSPM countries from 2018-2019 and between 2020-2022, with work ongoing. Data are also consulted by researchers and policymakers wishing to learn about and from reforms in other countries. This workshop specifically aims to: • Inform participants about the methodologies used by the HSPM network to track reforms • Highlight common trends in major health reforms across countries since 2018 • Discuss national health reforms and the value of understanding and tracking these across countries • Identify research gaps in reform monitoring relevant to policy makers • Discuss how the health system research community can generate more relevant and actionable evidence Added-value The comparative analysis of reform trends provides insights into how systems operate and the types of reforms more likely to be implemented. The sharing of these insights also serves as a source of inspiration, allowing countries to learn from each other’s successes and failures and adopt proven best practices. This is relevant for policymakers and researchers striving to support health systems strengthening. Therefore, the workshop will support knowledge exchange between researchers and policymakers by (1) informing researchers and policymakers about similar ongoing reforms in other countries and (2) identifying areas where researchers can contribute to country reform agendas, and (3) stimulating interest in cross-country collaborative research on similar and topical reforms in different countries. Format This is a roundtable workshop chaired and moderated by the Observatory. An overview of reform trends and patterns since 2018 will set the scene, followed by an interactive discussion with policymakers and HSPM network members to (1) delve into trend dynamics and implications for health policy and research and (2) identify major (missing) research areas relevant to policymakers. In addition, we will leverage the geographic scope and expertise of the audience to reflect on the value of monitoring reforms. Key messages • Cross-country exchange of information on reform trends and implementation patterns can inspire future reforms and help policymakers identify promising policy approaches abroad. • Knowledge exchange between researchers and policymakers helps to determine evidence gaps and to generate relevant and actionable evidence that can drive policy innovation. Speakers/Panelists Reinhard Busse Berlin University of Technology, Berlin, Germany Ines Fronteira National School of Public Health - NOVA University of Lisbon, Lisbon, Portugal Iwona Kowalska-Bobko Jagiellonian University Medical College, Institute of Public Health, Cracow, Poland Isabel de La Mata DG SANTE, European Commission, Luxembourg, Luxembourg Vesna-Kestrin Petric Ministry of Health, Ljubljana, Slovenia

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.058
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0130.011
Open science0.0020.007
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0580.022

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.

Opus teacher head0.168
GPT teacher head0.356
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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