Assessing needs for linguistic interpretation in hospital settings: A retrospective analysis of ad hoc interpreter requests
Bibliographic record
Abstract
Background:In Canada, healthcare professionals often rely on ad hoc interpreters, who are untrained volunteers recruited via intercom hospital announcements to interpret for patients with language barriers. This study analyzed the frequency of ad hoc interpreter requests via intercom announcements to estimate hospital interpretation needs.Methods:A retrospective cohort analysis from intercom requests for medical interpretation collected from five hospitals of the McGill University Health Center. Requests included date, time, language requested, hospital location, and extension for who placed the request.Results:A total of 1265 intercom requests were placed for 48 languages, with the top five languages being Mandarin (17.8%), Punjabi (10.1%), Inuktitut (9.8%), Arabic (7.3%), and Cantonese (6.4%). Almost 69.8% of requests were made during working hours, 13.2% on workday evenings, and 14.8% on weekends. Requests came from urgent care (42.3%), outpatient (29.5%), and inpatient (23.3%) settings.Conclusion:This is the first published study that measures interpretation needs via intercom requests. We propose that our method can be replicated to inform implementation of professional medical interpretation services. We conclude that linguistic interpretation needs are significant in the Montreal area, and likely in Canada in general and pose a barrier to effective medical care.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".