Nurse adaptability: Implementing clinical trials in the midst of a pandemic
Bibliographic record
Abstract
Working in Manhattan, the center of the nations’ outbreak of the novel coronavirus-19 virus truly demonstrated how adaptable nurses are. During this time, multiple clinical research trials began at our academic medical center, NYU Langone Health, as researchers attempted to learn what medical interventions worked best to treat critically-ill COVID-19 patients. In designing and implementing these trials, the researchers had little familiarity with the workings of inpatient hospital units. They did not understand how nursing staff provided care to patients on these units. Likewise, many bedside nurses had never assisted researchers in conducting clinical research on their patients. Therefore, a nursing operations team (NOT) was needed to assist both the research teams and the inpatient nurses. NOT met with the researchers to review proposed clinical research trials and determine how nursing staff would be utilized to complete the required research tasks such as specimen and data collection, study intervention administration, and patient monitoring. Toward that end, NOT developed education and training materials on all of the research trials that were implemented at NYU Langone Health for our bedside nurses. This education included tip sheets, safety huddle rounds with the involved units, and “just in time” education to any nurse whose patient was urgently enrolled in a trial. In this way, NOT helped bedside nurses quickly adapt to their role in assisting the research team conduct their studies on our COVID positive inpatients.
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 | 0.578 | 0.514 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".