Bibliographic record
Abstract
As contemporaneous data emerge from publicly funded healthcare providers, the COVID-19 pandemic provides a unique opportunity to measure their resiliency. Resiliency matters because it connotes a higher level of confidence in being able to provide needed healthcare during times of health, social or environmental stress or calamity. At the beginning of the first wave of the COVID-19 pandemic in early 2020, there were warnings regarding hospitals' ability to successfully manage large surges of critically ill COVID-19 patients who were expected to soon be presenting at hospitals in every province and territory. Shortly thereafter, hospitals implemented policies to clear hospital beds - there were public reports that hospitals rapidly went from nearly full occupancy to below 50% (CIHI 2020a; Howlett 2020; Zeidler 2020).
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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.032 | 0.042 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".