Inuit interpreters engaged in end-of-life care in Nunavik, Northern Quebec
Bibliographic record
Abstract
BACKGROUND: Inuit interpreters are key players in end-of-life (EOL) care for Nunavik patients and families. This emotionally intensive work requires expertise in French, English and Inuit dialects to negotiate linguistic and cultural challenges. Cultural differences among medical institutions and Inuit communities can lead to value conflicts and moral dilemmas as interpreters navigate how best to transmit messages of care at EOL. OBJECTIVES: Our goal was to understand the experience of Inuit interpreters in the context of EOL care in Nunavik in order to identify training needs. DESIGN: In the context of a larger ethnographic project on EOL care in Nunavik, we met with 24 current and former interpreters from local health centres and Montreal tertiary care contexts. Data included informal and formal interviews focusing on linguistic resources, experiences concerning EOL care, and suggestions for the development of interpretation training. RESULTS: Inuit working as interpreters in Nunavik are hired to provide multiple services of which interpretation plays only a part. Many have no formal training and have few resources (e.g. visual aids, dictionaries) to draw upon during medical consultations. Given the small size of communities, many interpreters personally know their clients and often feel overwhelmed by moral dilemmas when translating EOL information for patients and families. The concept of moral distress is a helpful lens to make sense of their experience, including personal and professional repercussions. CONCLUSIONS: Inuit interpreters in Nunavik are working with little training yet in context with multiple linguistic and cultural challenges. Linguistic and cultural resources and focused training on moral dilemmas unique to circumpolar contexts could contribute to improved work conditions and ultimately to patient care..
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How this classification was reachedexpand
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".