Shrinking the language accessibility gap: a mixed methods evaluation of telephone interpretation services in a large, diverse urban health care system
Bibliographic record
Abstract
INTRODUCTION: Language interpretation services for patients who are not proficient in a country's official language(s) are essential for improving health equity across diverse populations, and achieving clinical safety and quality for both patients and providers. Nevertheless, overall use of these services remains low, regardless of how they are delivered. In Toronto, Ontario, one of the most ethnically diverse urban centres, the regional local health integration network which oversees the highest concentration of health care organizations servicing 1.2 million residents, partnered with key stakeholders to make Over-the-Phone (OPI) interpretation services broadly and economically available in 170 different languages to its diverse network of health care organizations. This evaluation aimed to assess patients' and providers' experiences with OPI in these varied settings and the impact (if any) on alternative interpretation services and on health service delivery access and quality. METHODS: This study used a two-phased sequential exploratory mixed-methods approach to evaluate the initiative. Phase I was comprised of semi-structured interviews with representatives from the program stakeholders; these findings were applied to identify appropriate survey questions and response categories, and provided context and depth of understanding to Phase II results. Phase II included web-based and self-administered surveys for both providers and patients engaging with OPI. RESULTS: Both providers and patients identified a broad range of positive impacts OPI had on health care service delivery quality and access, and high levels of satisfaction with OPI, in a variety of health care settings. Providers also revealed a marked decrease in the use of ad-hoc, nonprofessional strategies for interpretation after the implementation of OPI, and noted it had either no impact on their workload or had decreased it overall. CONCLUSIONS: OPI is clearly not the sole answer to the complex array of health care needs and access gaps that exist for persons without proficiency in their country's official language. Nevertheless, this evaluation provides compelling evidence that OPI is a valuable component, and that it may contribute to a broader range of positive impacts, and within a broader range of health care settings, than previously explored.
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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 | 0.065 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".