Bibliographic record
Abstract
The intensity of the COVID-19 pandemic has tested the mettle of political, healthcare and public health leaders over the past year. Amid the unfolding events, healthcare leaders, including many nurses, have been pivoting, innovating, collaborating, safeguarding, inspiring and navigating - all the while informing the creation of an effective playbook to wage a counterassault for all of us. Despite all efforts, this previously unseen opponent has been unrelenting. Having been in the eye of the storm during the severe acute respiratory syndrome (SARS) outbreak, my memories of the events remain vivid. It was a time rife with uncertainty and fear, forcing the creation of a playbook on the basis of the best evidence and common sense but without the benefit of a precedent. Over the course of several months, our leadership was challenged by efforts to contain the virus and mitigate the very real possibility of a globally emerging pandemic. However, SARS was but a microcosm of the present situation. The COVID-19 pandemic is not like any other crisis we have experienced in our collective lifetime. We can only imagine the toll of this pandemic when it is finally over. It will be measured in terms of post-pandemic posttraumatic stress disorder, deaths from COVID-19 and delayed care, and deaths by suicide among healthcare workers and citizens; in the end, it will not be trivial. Those contributing to the COVID-19 playbook have given their all, and we should be eternally grateful to every single one of them.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.285 | 0.114 |
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".