Medical Interpreting – A Race against Time
Bibliographic record
Abstract
There are several factors that make medical interpreting particularly difficult, ranging from the emotional burden interpreters have to bear to terminological problems, from ethical issues to role confusion and relational complications. Interpreting tasks are made even more complicated by time constraints. In emergency situations time may even be a matter of life and death, so finding ways to avoid the wasting time is essential. This paper looks at ways new technologies are currently used to improve medical interpreters’ reaction times in the most advanced countries in this field (US, UK, Australia and Canada) and in the three countries participating in the ReACTMe project: Spain, Romania and Italy. The situation is examined from the point of view of the advantages and risks of using remote interpreting in medical settings, of the availability and efficiency of existing tools, and, last but not least, of the possible improvements in the countries of our project. The aim is to identify and disseminate methods and practices that can aid healthcare institutions and provide the basis for new training programmes that make full use of the different modes of remote interpreting.
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 | 0.048 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".