Stigma and Stereotyping of Veterans who May Benefit from a Psychiatric Service Dog: A Test of the Stereotype Content Model and Weiner’s Attribution-Affect-Action Model
Bibliographic record
Abstract
Among veterans with posttraumatic stress disorder (PTSD) and comorbid substance use disorder (SUD), stigma has been identified as a frequent barrier to help-seeking and treatment. Stigma can also lead to the activation of stereotypes and discrimination. Increasingly, veterans are relying on psychiatric service dogs (PSDs) to aid in managing their PTSD and comorbid SUD. Consumer demand currently exceeds the availability of PSDs in Canada, and very few individuals can afford a PSD independently. This study examined the stigma and stereotyping of Canadian veterans needing a PSD using two theoretical models of stereotyping and stigma. Hypotheses were mostly supported. The veteran with PTSD was positively stereotyped by participants and was considered part of the normative social “in-group.” Participants were more willing to support these individuals in their acquisition of a PSD. In contrast, a veteran with SUD was more negatively stereotyped and stigmatized. Societal perceptions of veterans with PTSD may be more positive than previous research findings. However, an accompanying SUD may counteract this and result in stigma, negative stereotyping, and decreased civilian support for veterans accessing PSDs. Future research on these issues is warranted, including the development and testing of interventions aimed at addressing beliefs about SUD and increasing empathy and compassion for individuals with SUD.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".