Measuring the COVID-19 treatment efficiency in OECD countries: a multiplicative network DEA approach
Bibliographic record
Abstract
The outbreak and subsequent COVID-19 pandemic became an urgent public hygiene crisis and concern for international society. The significance of evaluating a healthcare system’s performance when dealing with public hygiene hazards generally reflects the preparation and reaction level of a country. Data envelopment analysis (DEA) can assess efficiency by comparing outputs produced by inputs in each decision-making unit (DMU). This research thus employs a multiplicative DEA approach relative to a log-linear technology to construct a network DEA model that measures overall efficiency in a two-stage network structure. The stage efficiencies via a weighted geometric mean aggregate into overall efficiency. We decompose the weighted geometric mean efficiency through means of using the general two-stage structure as a numerical example. Some interesting findings about the change in overall and stage efficiencies appear. First, a variation in the weight of stage efficiency does not change the stage efficiency scores. Second, the stage efficiency scores for the most part remain unchanged under different weights of stage efficiency. Finally, we apply the proposed network DEA model in multiplicative form to evaluate the efficiency of COVID-19 treatment in OECD countries.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".