Addressing prioritization in healthcare amidst a global pandemic
Bibliographic record
Abstract
Trade-offs abound in healthcare yet depending on where one stands relative to the stages of a pandemic, choice making may be more or less constrained. During the early stages of COVID-19 when there was much uncertainty, healthcare systems faced greater constraints and focused on the singular criterion of "flattening the curve." As COVID-19 progressed and the first wave diminished (relatively speaking depending on the jurisdiction), more opportunities presented for making explicit choices between COVID and non-COVID patients. Then, as the second wave surged, again decision makers were more constrained even as more information and greater understanding developed. Moving out of the pandemic to recovery, choice making becomes paramount as there are no set rules to lean back into historical patterns of resource allocation. In fact, the opportunity at hand, when using explicit tools for priority setting based on economic and ethical principles, is significant.
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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 | 0.078 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".