Consequences of vicarious traumatization among mental health service providers with a history of child maltreatment: A narrative review.
Bibliographic record
Abstract
OBJECTIVE: This narrative review synthesizes existing literature evaluating the psychological and physiological impacts of vicarious trauma (VT), secondary traumatic stress (STS), and compassion fatigue (CF) in mental health providers (MHPs) with a personal history of childhood maltreatment. It also evaluates the impact of MHP childhood maltreatment history on the risk for developing VT, STS, and CF. METHOD: We conducted electronic database searches and forward and backward citation chaining to identify relevant articles. We extracted and synthesized data via iterative readings of each article, and then grouped articles into key thematic areas. RESULTS: We synthesized 10 studies. The studies identified a variety of psychological consequences of STS, CF, and VT, including increased risk of burnout, disruptions in cognitions, and altered worldviews. No studies examined the physiological consequences of STS, CF, or VT, highlighting a significant research gap. Nine of the 10 studies evaluated child maltreatment as a risk factor for STS, CF, or VT, with only five of these nine finding an association. CONCLUSION: Prevalence of maltreatment, especially emotional abuse and neglect, is high in the MHP population and may contribute to ongoing vulnerability to STS, CF, and VT. Childhood exposure to sexual abuse could have implications for MHPs' own intimate relationships. Studies with sample sizes capable of explicating the role of each type of child maltreatment for STS, CF, or VT among MHPs are needed. In addition, it would be prudent to regularly collect data on STS, CF, and VT experiences alongside physiological and psychological outcomes among the MHP workforce. (PsycInfo Database Record (c) 2023 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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".