Bibliographic record
Abstract
After a year of living a masked, isolated, virtual existence, there is much reflection among healthcare decision makers and providers around the world. What have we done well? What could we have done better? And more importantly, how will we ensure that our learnings inform decisions and actions the next time? In this latest installment of crisis leadership papers, authors address the toll exacted upon our profession thus far. Although profound, the psychological sequelae of the COVID-19 pandemic are directly related to a number of pre-existing conditions that have been festering below the surface for several years. In particular, blame for the state of health inequities, ageism, staff shortages and workplace violence cannot be ascribed to the pandemic. Rather, each has been exacerbated because of it.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.113 | 0.044 |
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".