Examining Changes in Posttraumatic Stress Disorder Symptoms and Substance Use Among a Sample of Canadian Veterans Working with Service Dogs: An Exploratory Patient- Oriented Longitudinal Study
Bibliographic record
Abstract
Comorbid posttraumatic stress disorder (PTSD) and substance use (SU) is a growing health concern among Canadian veterans. Veterans are increasingly seeking symptom relief for PTSD and comorbid SU by engaging service dogs (SDs). Despite promising results, the efficacy of SDs in aiding veterans warrants further investigation. An exploratory patientoriented, longitudinal, time-series, mixed-methods research design was employed with a sample of five Canadian veterans matched with SDs from AUDEAMUS, Inc. PTSD and SU were measured at six time points over 1 year with the Posttraumatic Stress Disorder Checklist for the Diagnostic and Statistical Manual for Mental Disorders, 5th Edition (PCL-5), Drug Use Screening Inventory Revised Substance Use Subscale (DUSI-R SU), and one-onone semi-structured interviews. There were clinically significant decreases in the veterans’ PTSD scores with the PCL-5. Interview content complemented these results. Veterans offered accounts of ways in which their SDs directly supported and helped manage their PTSD and related symptoms. While DUSI-R SU scale changes were non-significant, during interviews each veteran reported a decrease in their use of opioids and alcohol, while some reported an increase in their use of medical cannabis. However, veterans also highlighted ways in which their SDs sometimes contributed to increases in their PTSD and related symptoms, as well as their SU. This was particularly evident during the early stages of training and bonding. This study makes an important contribution to the emerging field examining the potential benefit of SDs for veterans diagnosed with PTSD. Additionally, this study is novel in its identification of the SDs beneficial contributions to veterans’ comorbid problematic use of substances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".