Caring for resilience:A knowledge agenda for health systems research in the Netherlands
Bibliographic record
Abstract
On the 21st of May 2021, the directors of the Erasmus Medical Center, Erasmus University Rotterdam, and the Delft University of Technology officially opened the Pandemic and Disaster Preparedness Center (PDPC). The PDPC is a collaborative network that seeks to prepare Dutch society for future pandemic and disasters, amongst others by initiating and facilitating innovative research into related and relevant topics. Specifically, the PDPC focusses on four key themes, including their crossovers: i) pandemic preparedness, ii) disaster preparedness, iii) societal preparedness, and iv) health systems resilience. An earlier study has identified the key questions for the first three themes. In this current report we zoom in on the fourth theme and identify the most pressing research gaps and remaining knowledge questions about health systems resilience in relation to the Dutch health system. We would like to thank our interviewees for participating in our study and are thankful for the financial support of the PDPC which enabled this project. Finally, we extend our gratitude to Linda Jansen, Jeannette de Boer, Valérie Eijrond, and Eline Boezelman for helping us in organising the working conference on health systems resilience in Utrecht.
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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 | 0.077 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.036 | 0.044 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".