Service Dog Schools for PTSD as a Tertiary Prevention Modality: Assessment Based on Assistance Dogs
Bibliographic record
Abstract
Psychiatric service dogs compensate in terms of social and physical cognition for people who suffer from chronic post-traumatic stress, reassuring them with their canine behavior in public places, at home and in relationships interpersonal skills with strangers. There are no certification and standards for schools that train service dogs in Canada and the United States. Does the fact that training is different from one school to another have an impact on the effectiveness of the assistance dog for his master? To identify all aspects that closely reflect tertiary prevention, this exploratory case study documents the processes and services supporting the assignment of service dogs to veterans with PTSD and the subsequent follow-up conducted at various dog training schools; and it evaluates and compares the processes and services in place. The case study included four data collection methods involving 31 veterans, 7 school delegates, 7 trainers and 23 dogs. Qualitative content analysis and all the information collected was rated according to the Theoretical Domains Framework (TDF) and Assistance Dogs International (ADI) criteria. Results indicated a TDF-scoring across 12 domains ranged from 6/24 to 16/24. The schools moderately reflected ADI-standards. Tertiary prevention recommendations were proposed for dog trainers to better address the domains that needed improving at the time of the study (knowledge about PTSD, beliefs about capabilities, behavioral regulation, environmental context and resources, beliefs about consequences, nature of behaviors).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".