Challenging Forensic Stigma: The Efficacy of Education and Indirect Contact Interventions in Addressing Stigma Towards Forensic Patients
Bibliographic record
Abstract
This study examined whether education, indirect contact, and a combination of education and indirect contact was associated with lower scores of forensic stigma. Undergraduate students ( N = 698) were randomly assigned into one of four conditions: An education video that provides empirical evidence challenging the myths about forensic patients, a contact video using clips from a documentary about a real forensic patient, a combination of both education and contact videos, and a control video providing general psychology facts. Participants completed questions about demographic characteristics, previous education in forensic psychology, and contact with forensic patients, as well as completing the Forensic Stigma Scale-Revised which measures the key stereotypes driving forensic stigma (i.e. dangerousness/unpredictability and responsibility/blame). Overall, participants in the education and combined condition had the lowest scores of forensic stigma. Participants in the contact condition scored similar to the control group. There were significant effects for gender (men and women) within conditions. The findings support the use of education interventions as an effective means of addressing stigma towards forensic patients.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".