Language barriers and epistemic injustice in healthcare settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Contemporary realities of global population movement increasingly bring to the fore the challenge of quality and equitable health provision across language barriers. While this linguistic challenge is not unique to immigration contexts and is likewise shared by health systems responding to the needs of aboriginal peoples and other historical linguistic minorities, the expanding multilingual landscape of receiving societies renders this challenge even more critical, owing to limited or even non-existing familiarity of modern and often monolingual health systems with the particular needs of new linguistic minorities. The centrality of language to health beliefs, attitudes, practices, cultural scripts, and conceptual frameworks emphasizes its pivotal role in the healthcare process, and consequently in the adverse effects of treatment that is language-insensitive and unaware. Such an attitude on the part of medical authorities risks considerable epistemic injustice in the form of a (mis)judgement of patients' intelligence, credibility, and rationality based on the language that they speak and the manner in which they speak it, consequently impacting the quality and equity of care provided. This danger, I argue, may be effectively countered by fostering among the participants in the healthcare process a sense of epistemic humility through greater metalinguistic awareness. Outlining a range of operative steps that can be used to facilitate this. I argue that the reality of language barriers in the healthcare process, while not entirely eliminable, may nevertheless be successfully addressed, in order to mitigate the challenge of quality and equitable healthcare provision in multilingual societies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it