86 The emotional impact on professional healthcare interpreters of interpreting palliative care conversations for adult patients: a rapid review
Bibliographic record
Abstract
Background Professional healthcare interpreters improve patient outcomes for patients with Limited English Proficiency, both in and outside of palliative care. Healthcare professionals working within palliative care are at risk of psychological distress with exposure to often challenging conversations, but the impact on interpreters working in this setting is insufficiently explored. We aimed to synthesise existing findings into the emotional effects of conducting palliative care conversations on this core member of the healthcare team. Methods A rapid review of five electronic databases was conducted in December 2021. Studies available in English identifying emotional effects on professional healthcare interpreters of interpreting common palliative care conversations for adult patients, were searched for inclusion. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework guided the review. Quality appraisal was performed using CASP checklists. Thematic analysis was conducted using NVivo. Quotes were utilised to illustrate themes. Results 11 articles were included for analysis from the USA(5), Australia(3), Canada(2) and the UK(1). 8 interview-based, 2 online surveys and 1 quality improvement project. From the reviewed papers, themes were identified under three categories. (1) Emotional effects: including stress, discomfort, loneliness, guilt. (2) Factors Influencing Emotional Effects: moral conflicts and the role of the interpreter, perceived clinician communication, barriers to seeking support, relational and interpreter factors. (3) Recommendations to mitigate negative emotional effects: pre-briefing, debriefing and interpreter/provider training. Conclusion Interpreters experience a range of emotional responses to palliative care conversations. Moral conflict resulted when expectations of the interpreter’s role were unclear; when interpreting verbatim (acting as a neutral conduit) clashed with the desire to deliver information in a culturally sensitive way (acting as a cultural broker). Improving role clarity and collaborative clinician-interpreter training may alleviate negative emotional effects. Evidence limited by rapid nature of review and location/heterogeneity of studies.
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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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".