Personal trauma history and secondary traumatic stress in mental health professionals: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Caring for those who have been traumatized can place mental health professionals at risk of secondary traumatic stress, particularly in those with their own experience of personal trauma. AIM: To identify the prevalence of personal trauma history and secondary traumatic stress in mental health professionals and whether there is an association between these two variables in mental health professionals. METHOD: We preregistered the review with PROSPERO (CRD42022322939) and followed PRISMA guidelines. Medline, Embase, PsycINFO, Web of Science and CINHAL were searched up until 17th August 2023. Articles were included if they assessed both personal trauma history and secondary traumatic stress in mental health professionals. Data on the prevalence and association between these variables were extracted. Quality assessment of included studies was conducted using an adapted form of the Newcastle-Ottawa scale. RESULTS: A total of 23 studies were included. Prevalence of personal trauma history ranged from 19%-81%, secondary traumatic stress ranged from 19% to 70%. Eighteen studies reported on the association between personal trauma history and secondary traumatic stress, with 14 out of 18 studies finding a statistically significant positive relationship between these variables. The majority of studies were of fair methodological quality. DISCUSSION: Mental health professionals with a personal history of trauma are at heightened risk of suffering from secondary traumatic stress. IMPLICATIONS FOR PRACTICE: Targeted support should be provided to professionals to prevent and/or address secondary traumatic stress in the workforce.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".