Training and usage of language interpretation services among health care providers in a large Canadian pediatric centre
Bibliographic record
Abstract
Objectives: Canada has a diverse population and one of the highest immigration rates in the world. Non-English or French language preference populations face barriers accessing health care, which leads to increased rates of adverse events. Adequate training of health care providers (HCPs) with language interpretation services (LIS) and having accessible LIS are crucial to improve the quality of patient care. We sought to identify HCPs' prior training, and use of and attitudes toward LIS in a Canadian tertiary pediatric centre. Methods: An electronic survey was developed and distributed to a diverse group of pediatric HCPs at the Stollery Children's Hospital in Edmonton, Alberta between October 2022 and February 2023. Survey completion was anonymous and voluntary. Descriptive statistics were used for quantitative data and thematic analysis for free text responses. Results: Three hundred forty-five HCPs accessed the survey and 281 (81%) completed it. Fifty-three percent of respondents encountered a pediatric non-English or French language preference patient/family once weekly or more and 51.5% reported no previous training in the use of LIS in patient care. In-person interpreters were perceived as the most effective LIS modality. Barriers to using LIS included: inability to access desired modality and/or language; insufficient time; and lack of comfort in effective usage. Conclusions: HCPs perceive LIS as integral to patient care and convenient to use, yet most report no prior training. This study identifies multiple barriers to using LIS and suggested improvements include enhancing LIS accessibility, reliability, and LIS education. These results will help inform local LIS initiatives and resource allocation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".