Vicarious Growth, Traumatization, and Event Centrality in Loved Ones Indirectly Exposed to Interpersonal Trauma: A Scoping Review
Bibliographic record
Abstract
It is well-known that interpersonal traumatic events can impact the physical and mental health of those indirectly exposed to the events. Less studied are populations of loved ones who have been indirectly exposed to interpersonal trauma. We conducted a scoping review to synthesize literature related to potential consequences of indirect interpersonal trauma exposure, specifically vicarious traumatization (VT) and vicarious posttraumatic growth (VPTG). We used the Joanna Briggs Institute methodology. Inclusion criteria included: (1) participants were indirectly exposed to the interpersonal trauma of a loved one in adulthood, (2) discussion of VT, VPTG, or related terms, (3) published peer-reviewed empirical journal articles, and (4) available in English. We used a three-step search strategy to find relevant articles. Keywords found from the first two steps were entered into PsycINFO, PsycArticles, PubMed, Scopus, and Web of Science databases. Reference lists of the included articles were also examined. The identified articles were then screened using the inclusion and exclusion criteria. Twenty-eight articles met inclusion and exclusion criteria. Twenty-six articles referenced VT or related terms, one referenced VPTG, and one referenced vicarious trauma keywords. Results of this scoping review are summarized by definitions, measures, key findings, and knowledge gaps. Future research should focus on vocabulary management, diverse samples, and VPTG in this population, including the identification or creation of appropriate measures.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".