Preventing vicarious traumatization of mental health therapists: Identifying protective practices.
Bibliographic record
Abstract
This qualitative study identified protective practices that mitigate risks of vicarious traumatization (VT) among mental health therapists. The sample included six peer-nominated master therapists, who responded to the question, "How do you manage to sustain your personal and professional well-being, given the challenges of your work with seriously traumatized clients?" Data analysis was based upon Lieblich, Tuval-Mashiach, and Zilber's (1998) typology of narrative analysis. Findings included nine major themes salient across clinicians' narratives of protective practices: countering isolation (in professional, personal and spiritual realms); developing mindful self-awareness; consciously expanding perspective to embrace complexity; active optimism; holistic self-care; maintaining clear boundaries; exquisite empathy; professional satisfaction; and creating meaning. Findings confirm and extend previous recommendations for ameliorating VT and underscore the ethical responsibility shared by employers, educators, professional bodies, and individual practitioners to address this serious problem. The novel finding that empathic engagement with traumatized clients appeared to be protective challenges previous conceptualizations of VT and points to exciting new directions for research, theory, training, and practice. (PsycINFO Database Record (c) 2010 APA, all rights reserved).
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".