Intra-provincial benchmark analysis of COVID-19 in Canada
Bibliographic record
Abstract
The COVID-19 pandemic posed unheralded challenges to people, business, government at all levels (federal, provincial, regional), and society at large. In addition to the direct consequences of taking care of infected people, which in some countries led to a virtual collapse of the healthcare system, the pandemic strained eldercare, employment, economic growth, and exacerbated mental health and social problems. During the first year of the pandemic, researchers’ and policy makers’ main focus was on ‘flattening the curve,’ and on predictive modeling of infections and deaths. In this paper we present a non-parametric data-driven descriptive analysis based on Data Envelopment Analysis to assess COVID-19 in ten Canadian provinces over the two year period 2020 to 2021. The objective is to derive worst- and best-case intra-provincial benchmarks to assess if and to what extent the situation could have been worse respectively better. To take account for any indirect socio-economic impact our analysis incorporates official monthly unemployment rates and a stringency index reflecting the level of social policy restrictions imposed by the provincial governments. A major contribution of the model framework is that it provides a mechanism for measuring the impact of the two main strategies in curbing the pandemic, namely vaccination and social policy restrictions. As a robustness check, the bench-mark results are compared against bias-corrected efficiency measures.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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".