The emotional effects on professional interpreters of interpreting palliative care conversations for adult patients: A rapid review
Bibliographic record
Abstract
BACKGROUND: Professional interpreters working in palliative contexts improve patient care. Whilst literature identifies psychological distress in other healthcare professionals, research into emotional effects on professional interpreters in this highly emotive setting is limited. Isolating emotional responses may enable targeted interventions to enhance interpreter use and improve wellbeing. Timely evidence is needed to urgently familiarise the profession with issues faced by these valuable colleagues, to affect practice. AIM: Describe the emotional effects on professional interpreters of interpreting adult palliative care conversations. Collate recommendations to mitigate negative emotional effects. DESIGN: We performed a rapid review of studies identifying emotional effects on professional interpreters of interpreting adult palliative conversations. Rapid review chosen to present timely evidence to relevant stakeholders in a resource-efficient way. Thematic analysis managed using NVivo. Quality appraisal evaluated predominantly using CASP checklists. Reported using PRISMA guidelines. PROSPERO registration CRD42022301753. DATA SOURCES: Articles available in English on PubMed [1966-2021], MEDLINE [1946-2021], EMBASE [1974-2021], CINAHL [1981-2021] and PsycINFO [1806-2021] in December 2021. RESULTS: Eleven articles from the USA (5), Australia (3), Canada (2) and UK (1). Eight interview-based, two online surveys and one quality improvement project. Themes included (1) Identifying diversity of emotional effects: emotions including stress, discomfort, loneliness. (2) Identifying factors affecting interpreters' emotional responses: impact of morals, culture and role expectations; working with patients and families; interpreter experience and age. (3) Recommendations to mitigate negative emotional effects: pre-briefing, debriefing and interpreter/provider training. CONCLUSION: Professional interpreters experience myriad emotional responses to palliative conversations. Role clarity, collaborative working and formal training may alleviate negative effects.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".