Using Data Envelopment Analysis to Measure the Efficiencies of Saskatchewan’s Health Regions during the COVID-19 Pandemic
Bibliographic record
Abstract
We develop empirical bootstrap data envelopment analysis models to evaluate the relative efficiencies of Saskatchewan's various health regions in handling the health emergency situation created by the coronavirus (COVID-19) pandemic. Each region's relative success in managing the influx of COVID-19 cases was assessed based on input and output variables associated with efficiency. Our findings show that Saskatchewan has done a good job of handling the COVID-19 pandemic from a managerial point of view, but not in terms of the effective utilization of resources. We further use the data envelopment analysis models to identify the most efficient health regions, which in turn serve as benchmarks that the other regions can follow in improving their respective efficiencies. The findings of this study can assist decision makers in developing policies that will enable efficient management of the pandemic, and mitigate health inequities for populations in different health regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.021 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".