Language Barriers to Healthcare for Linguistic Minorities: The Case of Second Language-specific Health Communication Anxiety
Bibliographic record
Abstract
In this study we examined health communication anxiety (HCA) associated with language-discordant situations – that is, where people have to use their second language (L2) to communicate with health providers who are using their first language (L1). We adapted existing HCA scales in order to (1) assess L2 HCA in such situations separately for physical and mental/emotional health contexts and (2) control for potential confounds, such as HCA not related to L2 use and L2 communication anxiety not related to health, allowing us to obtain L2-specific measures of HCA. We examined the relationship between L2-specific HCA and willingness to use health services in language-discordant situations. English-speaking linguistic minority participants (N = 314) living in Québec, a predominantly French-speaking area of Canada, were recruited for online testing. The results revealed that, separately for both physical and mental/emotional health contexts, there were significant and meaningful L2-specific relations between HCA and willingness to use L2 health services – i.e., over and above general anxiety and discomfort about using an L2, and over and above general health communication anxiety. The effect was stronger for mental/emotional health contexts. The results are discussed in terms of their implications for understanding barriers to health services for linguistic minorities.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".