Building Capacity of Interpreting Services in Australian Healthcare Settings: The Use of Video Remote Interpreting during the COVID-19 Pandemic
Bibliographic record
Abstract
Confronted with a crisis of unprecedented scale in the second quarter of 2020, Australian States and Territories had to adopt crisis management strategies to ensure equitable access to services are guaranteed for all communities. In this context, and because face-to-face interpreting was no longer an option for each consultation, clinics, hospitals, and GP practices were urged to resort to remote interpreting, i.e., the use of technologies to gain access to an interpreter. After setting the Australian healthcare interpreting scene against historical milestones, this article discusses the usability of Video Remote Interpreting (VRI) in Australian healthcare settings, the way the demands for this new modality were met, as well as the perceptions of participants involved in remote communication. Findings from a mixed method study are presented and discussed. The data collected through surveys and interviews aimed to identify how and if the use of VRI proved efficient, and if this modality was expected to replace onsite and telephone interpreting and to what extent. The outcomes showed a shift from Telephone Interpreting to Video Remote Interpreting as the preferred remote modality, though onsite interpreting remains the preferred modality of the participants involved in the communication exchange.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".