Meta‐Analysis of Risk Factors for Secondary Traumatic Stress in Therapeutic Work With Trauma Victims
Bibliographic record
Abstract
Revisions to the posttraumatic stress disorder (PTSD) diagnostic criteria in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013) clarify that secondary exposure can lead to the development of impairing symptoms requiring treatment. Historically known as secondary traumatic stress (STS), this reaction occurs through repeatedly hearing the details of traumatic events experienced by others. Professionals who work therapeutically with trauma victims may be at particular risk for this exposure. This meta-analysis of 38 published studies examines 17 risk factors for STS among professionals indirectly exposed to trauma through their therapeutic work with trauma victims. Small significant effect sizes were found for trauma caseload volume (r = .16), caseload frequency (r = .12), caseload ratio (r = .19), and having a personal trauma history (r = .19). Small negative effect sizes were found for work support (r = -.17) and social support (r = -.26). Demographic variables appear to be less implicated although more work is needed that examines the role of gender in the context of particular personal traumas. Caseload frequency and personal trauma effect sizes were moderated by year of publication. Future work should examine the measurement of STS and associated impairment, understudied risk factors, and effective interventions.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".