Training and Integrating Public Service Interpreters in a Refugee Health Clinic: A Mixed-Method Approach to Evaluate an Innovative Program
Bibliographic record
Abstract
Language barriers can harm refugees’ health, and trained interpreters are a solution to overcome these barriers in all health consultations. This study trained interpreters and integrated them in a refugee clinic. Nepali-speaking migrants were recruited and underwent 50 hours of training to serve as interpreters for recently arrived Bhutanese refugees in Quebec City. To evaluate the project, mixed data were collected. At baseline and follow-up, patients’ health (as perceived by practitioners) and satisfaction were evaluated. Interpreters and practitioners were also interviewed and took part in joint discussion workshops. Patients’ health remained stable but, interestingly, patients were slightly less satisfied at follow-up. Practitioners and interpreters described both benefits and difficulties of the program. For example, integrating interpreters within the clinical team allowed for better collaboration and mutual knowledge of cultures. Challenges included work overload, conflicts between interpreters and practitioners, and role conflicts for interpreters. Overall, the full-time integration of trained interpreters in the clinic facilitated communication and case administration. This practice could be especially beneficial for refugee clients. In future interventions, interpreter roles should be better clarified to patients and practitioners, and particular attention should be paid to selection criteria for interpreters.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".