Interpreting is an emotional effort: Public service interpreters’ coping strategies in emotionally charged situations
Bibliographic record
Abstract
Public service interpreters are at risk of burnout and vicarious trauma, as are all practitioners in the helping professions. A few rare studies on this subject show the almost systematic absence of support offered to interpreters to face these challenges. Furthermore, training and theories describing the practice of interpreting ignore the emotional aspect of this type of work. The main objective of this study is to highlight the strategies used by interpreters to cope with emotionally charged interventions. Following an exploratory and qualitative design, 21 sign and speech language interpreters agreed to recount a significant event in their careers. These narratives were subjected to a deductive thematic analysis according to three theoretical frameworks, thus allowing to highlight the relational dynamics during an interpreted intervention (stances and positionings) and the necessary cognitive efforts. Four coping strategies emerged from the analysis. Two are collaborative and seem to lead to a satisfactory resolution of the event, even if the emotional load is strong and poses significant challenges during the intervention. The other two have an obstructive impact on the communication. Taken together, the results of this study show that public service interpretation is an emotional as well as a cognitive and relational effort.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".