Reflexiones sobre cómo evaluar y mejorar la respuesta a la pandemia de COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has hit Spain particularly hard, despite being a country with a developed economy and being praised for the robustness of its national health system. In order to understand what happened and to identify how to improve the response, we believe that an independent multi-disciplinary evaluation of the health, political and socio-economic spheres is essential. In this piece we propose objectives, principles, methodology and dimensions to be evaluated, as well as outlining the type of results and conclusions expected. Inspired by the requirements formulated by the WHO Independent Panel for Pandemic Preparedness and Response and by experiences in other countries, we detail the multidimensional aspects to be evaluated. The goal is to understand key aspects in the studied areas and their scope for improvement in terms of preparedness, governance, regulatory framework, national health system structures (primary care, hospital, and public health), education sector, social protection schemes, minimization of economic impact, and labour framework and reforms for a more resilient society. We seek to ensure that this exercise serves not only at present, but also that in the future we are better prepared and more agile in terms of our ability to recover from any pandemic threats that may arise.
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 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 | 0.119 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".