Does the part-time victim services volunteer suffer from the effects of vicarious traumatic stress?
Bibliographic record
Abstract
This unique research into whether part-time victim services volunteers suffer from the effects of vicarious traumatic stress is the first of its kind because all of the participants were volunteers and members of victim services units in Southern Ontario, Canada. The aim was to determine whether volunteers suffered from personal trauma in their role as victim services volunteers with the objective of enhancing future volunteer training programmes. Two surveys were used to determine the existence of stressors in the lives of these volunteers, the Professional Quality of Life survey (ProQOL) and the Civilian version of the Post Traumatic Stress Disorder Checklist (PCL-C). The ProQOL was used as a written survey and the PCL-C was used as a foundation for interview questions in order to gain insight into the issues surrounding the effect of stressors. Results indicate that Victim Services volunteers suffer from the effects of dealing with victim’s traumatic events in varying degrees. Some suffer from burnout and secondary stress. While most appear to receive satisfaction from the role as a victim services volunteer, many would benefit from enhanced debriefing processes and enhanced training about how vicarious traumatic stress is recognized and successfully managed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".