The Long-Term Psychological Effects of Violent Crime on Victims: A Review of Evidence and Supportive Interventions
Bibliographic record
Abstract
This study utilizes anonymous survey data collected from 26 mental health professionals in various regions of Canada to understand which psychological disturbances are the most prevalent among victims of violent crime as well as which psychological interventions are most beneficial for treating violent crime victims. The data revealed that Post-Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), Generalized Anxiety Disorder (GAD), anger, guilt, self-blame, and shame are each highly prevalent. Findings also revealed that certain therapeutic methods including Eye Movement Desensitization Reprocessing (EMDR), Supportive Counselling (SC), Progressive Relaxation (PR), Cognitive Reprocessing Therapy (CPT) and Cognitive Restructuring (CR) are associated with advantageous outcomes for victims. Additionally, the findings also establish the importance of several components in promoting therapeutic success, including the integration of multiple therapies, an individualized, trauma-informed approach, victim readiness and willingness to engage in treatment, strong family and social support, clinician cultural competency, and continuous follow-up care. The final chapter of this thesis summarizes the core findings of the study and provides a discussion of the limitations of this study as well as suggestions for further research.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".