Effectiveness of Operation K9 Assistance Dogs on Suicidality in Australian Veterans with PTSD: A 12-Month Mixed-Methods Follow-Up Study
Bibliographic record
Abstract
Post-traumatic stress disorder (PTSD) is a pervasive disorder among both current and ex-serving Australian Defence Force (ADF) members. Studies have shown current psychological and pharmacological treatments for PTSD are suboptimal in veterans, with high dropout rates and poor adherence to treatment protocols. Therefore, evaluating complementary interventions, such as assistance dogs, is needed for veterans who may not receive the ultimate benefit from traditional therapies. The present longitudinal mixed-method study examined the effectiveness of Operation K9 assistance dogs among sixteen veterans with PTSD, specifically, their effects on suicidality, PTSD, depression, and anxiety from baseline to 12 months post-matching. Self-reported measures were completed prior to receiving their dog (baseline) and at three time points (3, 6, and 12 months) following matching. The Clinician-Administered PTSD Scale for DSM-5 was used to assess the severity of every PTSD case. Veterans participated in a semi-structured interview 3 months post-matching. Whilst there was a reduction in the proportion of veterans reporting any suicidality, there was no significant change in the probability of veterans reporting suicidality between time points. There was a significant effect of time on PTSD, depression, and anxiety symptoms. Three major themes emerged from qualitative data analysis: life changer, constant companion, and social engagement. Qualitative data suggest assistance dogs can have a positive impact on important areas of daily life and support veterans in achieving some of the prerequisites for health, including access to services, transport, education, employment, and development of new and diverse social and community connections. Connections were key in improving health and wellbeing. This study exemplifies the power of human-animal relationships and adds emphasis to the need to take these seriously and create supportive healthy environments for veterans with PTSD. Our findings could be used to inform public health policy and service delivery, in line with the Ottawa Charter action areas and indicate that for veterans with PTSD, assistance dogs may be a feasible adjunct intervention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".