Access to Translator (AT&T) project: Interpreter on Wheels during the COVID-19 pandemic
Bibliographic record
Abstract
We report our experience in using virtual technology in our emergency department (ED) to meet communication needs of our patients who have limited English proficiency (LEP) during the COVID-19 pandemic. Our project aim was to improve communication between our ED staff and patients who have LEP. Specifically, our primary aim was to eliminate the use of healthcare staff as ad hoc interpreters by 50% in our ED by using virtual medical interpreters within 2 months. To achieve our goal, several strategies were employed. First, we assessed the need for interpreters in our ED by tracking the number of times our nursing staff is pulled away from their nursing role to help other staff as an ad hoc interpreter. Second, a patient survey was conducted to understand their thoughts and needs for interpretation in the ED. Third, we developed strategies in improving access to interpreters in our ED. During the COVID-19 pandemic, we conducted a trial of using 'Interpreter on Wheels' (IOW) in our ED. In a 2-month period, we had 477 virtual interpretation encounters totaling 4123 interpretation minutes of IOW usage. We found that it satisfied not only our communication needs but also reduced some of our potential infection control risks during the pandemic.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".