Sign Language Interpreters and Burnout: Exploring Perfectionism and Coping
Bibliographic record
Abstract
Maslach (1982) conceptualizes burnout as emotional exhaustion and cynicism, which erodes an individual’s ability to effectively engage in work. A known antecedent to burnout across a variety of occupations, including interpreting, is chronic job stress (Delisle, Lariviere, Imbeau, & Durand, 2005; Swartz, 1999). The multidimensional construct of perfectionism is one personality trait noted in the literature (Flett & Hewitt, 2002) that affects how an individual perceives and manages stressors and it is consistently associated with burnout. Perfectionism is characterized by a tendency to set and strive for high personal standards and has both detrimental and beneficial potential (Stoeber & Otto, 2006). Investigators focusing on sign language interpreters have identified a wide range of cognitive and personality factors that contribute to the effective management of stress, such as perfectionistic traits (Bontempo & Napier, 2011). In contrast, negative appraisals of work, self-doubt and self-criticism are consistently associated with the development of burnout amongst sign language interpreters (Qin, Marshall, Mozrall, & Marschark, 2008; Schwenke, Ashby & Gnilka, 2014). Within the extant literature there is evidence that perfectionistic traits, influence an individual’s personal assessment of work demands and can contribute to the development of burnout. The current literature review explores the issue of burnout within the field of interpreting by considering the role of perfectionism, stress and coping, and lays the groundwork for additional research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".