How Much is Too Much: Vicarious Trauma and Healthcare Professionals
Bibliographic record
Abstract
When working directly with trauma survivors, healthcare professionals are likely to become attached and involved with clients and their lived experience.Due to the cumulative exposure to clients' traumatic stories, healthcare professionals often experience a phenomenon known as vicarious trauma.Symptoms of vicarious trauma include: depression, anxiety, restlessness, guilt, etc.For this study, I conducted six one-to-one, semi-structured qualitative interviews in Vancouver, Canada.All of the participants were individuals working in the healthcare field.The research findings indicated five major themes: background, experience with vicarious trauma, feelings and emotions, coping techniques and implications for future students.Findings suggest that vicarious trauma is common phenomenon among healthcare practitioners who work with trauma survivors.Unfortunately, there currently is a limited amount of research regarding why vicarious trauma is such an unrepresented aspect of the profession.Results of this study indicate that recreation and leisure activities such as art, music, exercise and mediation were found to be the most common coping techniques when experiencing vicarious trauma.Moreover, findings from this research study suggest a need to add selfcare and trauma training to educational programs for healthcare students.Therefore, early prevention and education regarding coping of vicarious trauma will prepare more resilient healthcare professionals.In turn, future professionals will be able to provide better care for clients, and can stay working in the field longer.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".