“I’ve Lost Some Sleep over It”: Secondary Trauma in the Provision of Support to Older Fraud Victims
Bibliographic record
Abstract
The Senior Support Unit (SSU) operates from the Canadian Anti-Fraud Centre and uses volunteers (all seniors themselves) to provide telephone support to older fraud victims across Canada. Many fraud victims have experienced significant trauma, and working with them to assist in their recovery can be difficult. Secondary trauma is well established in other contexts as affecting both professionals and volunteers who work with victims. However, secondary trauma has not been examined in the context of supporting fraud victims. Based on interviews with 21 SSU volunteers, it is argued that there are several indicators of secondary trauma evident in the experiences of the SSU volunteers. This article examines the challenges that exist in supporting fraud victims within a secondary trauma framework. This includes the distressing nature of the calls, maintaining boundaries, repeat victims, and suicidal victims. However, it also describes the coping mechanisms that the SSU volunteers have put in place to enable their continued support, primarily focusing on the positives and seeking collegial support. Despite the trauma associated with helping fraud victims, the SSU has developed a strong and positive culture that supports volunteers in that capacity. The article concludes with what can be learned from the SSU example.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".