Integrating New Staff into Endovascular Stroke-Treatment Workflows in the COVID-19 Pandemic
Bibliographic record
Abstract
A health care crisis such as the coronavirus disease 2019 (COVID-19) pandemic requires allocation of hospital staff and resources on short notice. Thus, new and sometimes less experienced team members might join the team to fill in the gaps. This scenario can be particularly challenging in endovascular stroke treatment, which is a highly specialized task that requires seamless cooperation of numerous health care workers across various specialties and professions. This document is intended for stroke teams who face the challenge of integrating new team members into endovascular stroke-treatment workflows during the COVID-19 pandemic or any other global health care emergency. It discusses the key strategies for smooth integration of new stroke-team members in a crisis situation: 1) transfer of key knowledge (simple take-home messages), 2) open communication and a nonjudgmental atmosphere, 3) strategic task assignment, and 4) graded learning and responsibility. While these 4 key principles should generally be followed in endovascular stroke treatment, they become even more important during health care emergencies such as the COVID-19 pandemic, when health care professionals have to take on new and additional roles and responsibilities in challenging working environments for which they were not specifically trained.
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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".