596 Implementing the right to interpretation in healthcare: challenges and successful strategies for policymakers, practitioners and patients
Bibliographic record
Abstract
Abstract WKS 20:, Implementing the Right to Interpretation in Healthcare: Challenges and Successful Strategies for Policymakers, Practitioners and Patients, B203 (FCSH), September 4, 2025, 14:45 - 15:45 Language barriers pose significant challenges to delivering quality refugee healthcare, and conveying effective preventive and public health information. A recent study in Canada showed that there were significant gaps and disparities accessing medical interpreting services. General access to medical interpreting is both a moral imperative and a prudent investment. In Canada, as most countries in the Global North, however there is an absence of a cohesive national strategy, reflected in diverse funding models employed across provinces. Goals To review experiences in North America and Europe with access to medical interpretation To discuss advocacy strategies for medical interpretation relevant to each context For the first half hour-drawing from a recent article https://www.mdpi.com/1660-4601/21/5/588 we will discuss the variation across Canadian provinces, human rights principles and ethical considerations including rights to healthcare and non-discrimination within the system, rights not to be harmed and to informed consent/refusal of individuals. and explain how medical interpreting enhances healthcare quality, preserves patient autonomy and builds trust along with decision-making processes for utilizing interpreting services and some digital strategies; For the second third we’ll discuss the European context drawing on experiences of participants. If the audience is large we can break into two or three groups. For the final third we can discuss policy changes required and how to implement them sharing experiences of all. We will share a useful framework for advocating for interpretation as a human right. We hope to develop recommendations at provider, organizational and system levels to ensure equitable access to promote the health and well-being of refugees in North American and European contexts.
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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 | 0.135 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.060 |
| Scholarly communication | 0.038 | 0.029 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.027 | 0.041 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".