COVID touch and travel data around US and foreign hospitals 2020-2021
Bibliographic record
Abstract
Disaster documentation has traditionally been considered a single location, post-event activity, but increasingly there is the recognition that disasters can impact multiple communities over a relatively short period of time and that there is significant value in understanding population response in near real-time, as a threat moves across communities. To this end, this study used on-the-ground observers to capture perishable data at more than a dozen domestic healthcare facilities (hospitals and urgent care centers) and similar hospital in 5 international locations. This was done for approximately 2 months at each location to observe street-level behavior of individuals leaving COVID-19 medical facilities. The documentation includes gender, touch behavior, mask usage, and choices of both destination and transportation. This project was a follow on to a similar, exclusively NYC-based study previously conducted over 9 weeks in the Spring 2020 at the onset of COVID-19. The project was coordinated out of New York University by Prof. Debra Laefer from the Tandon School of Engineering and Christopher Dickey of the School of Global Public Health but with significant support in Lebanon from Dr. Martine Najem of the America University of Beirut and in Mexico by Dr. Isidro Gutiérrez of the Autonomous University of Querétaro. The NYU team trained all domestic participants, the NYU students in Haiti, Russia, and Canada, and the local leadership teams in Queretaro and Beirut, who in turn trained their local observers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".