Barriers to the use of trained interpreters in consultations with refugees in four resettlement countries: a qualitative analysis using normalisation process theory
Bibliographic record
Abstract
Background: Increasing numbers of primary care practitioners in refugee resettlement countries are providing care to refugees. Access to trained interpreters is a priority for these practitioners, but there are many barriers to the implementation of interpreted consultations in routine care. There is a lack of international, theoretically informed research. The purpose of this paper is to understand barriers to interpreter use in primary care consultations in four resettlement countries using Normalisation Process Theory. Method: We conducted a cross-sectional online survey with networks of primary care practitioners (PCPs) who care for refugees in Australia, Canada, Ireland and the US (n = 314). We analysed qualitative data from the survey about barriers to interpreter use (n = 178). We completed an inductive thematic analysis, iteratively developed a Normalisation Process Theory (NPT)-informed coding frame and then mapped the emergent findings onto the theory’s construct about enacting interpreted consultations. Results: In all four countries, the use of an interpreter presented communication and interaction challenges between providers and patients, which can impede the goals of primary care consultations. Primary care practitioners did not always have confidence in interpreted consultations and described poor professional practice by some interpreters. There was variation across countries, and inconsistency within countries, in the availability of trained interpreters and funding sources. Conclusion: There are shared and differential barriers to implementation of interpreted consultations in a consistent and sustained way in the four countries studied. These findings can be used to inform country-specific and international level policies and interventions focusing on improving skills and resources for interpreted consultations to improve implementation of interpreted primary care consultations.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".