Bibliographic record
Abstract
Language is a social determinant of health, no less so in the case of Latinx persons, who make up the second largest ethnic group in the United States.In US health care, language and linguistic difference are often conceived in discrete, instrumental, and monolithic terms.This article characterizes this conception of language as administrative logic, which is in sharp contrast to language conceived as a richly complex, heterogeneous, communally lived human experience.This article emphasizes the importance of system-level language awareness and epistemic humility for promoting equity, as well as the need to avoid toonarrow focus on linguistic assessment.Administrative Logic Many residents of the United States, including health care workers, are aware that Latinx make up the second largest ethnic/racial minority group in the United States.Less often realized is the heterogeneity that underlies this classification.1 (We use the label Latinx throughout primarily for reasons of thematic and conceptual coherence with the rest of the contributions in the thematic issue, while recognizing the social significance and political salience of relevant competing or interrelated notions, such as Latinidad and Indigenidad.)In what follows, we outline the implications of the sociolinguistic context of US Latinx for their clinical care.More specifically, we consider the ways in which the quality and equity of care provided to US Latinx are affected by a profound tension between the Anglo-centric and often monolingual administrative logic of health care institutions, on the one hand, and the complexities of Latinx' linguistic lived experience of health and illness, on the other.More specifically, we offer a critique of this administrative logic in the context of health care, and we examine how a less regulative-centered conception of language and linguistic agency can better serve health care policies and practices committed to equality, diversity, and inclusion and likewise guide their development, implementation, and assessment.Heterogeneity in Latinx Linguistic Identities The goal of equitable health care delivery to US Latinx-and to other US demographic groups-has a critical linguistic dimension.Most studies show that linguistically concordant care improves health outcomes.2,3 Linguistically discordant care often leads to patient dissatisfaction, lower quality of care, inappropriate follow-up, and worse health outcomes.4,5 That is, care in a language other than that preferred by the patient
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 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".