Strengthening Zooeyia: Understanding the Human-Animal Bond between Veterans Living with Comorbid Substance Use and Posttraumatic Stress Disorder and their Service Dogs
Bibliographic record
Abstract
Zooeyia includes the potential benefits that interactions and relationships with companion animals can bring to human health. These potential benefits have been grouped into four components to describe the means through which they may occur: pets as builders of social capital, agents of harm reduction, motivators for health behavior change, and active participants in treatment plans. This construct has been used to examine the human-animal bond (HAB) and understand animal-assisted interventions. It has not, however, been intentionally applied within the context of military Veterans with posttraumatic stress disorder and comorbid substance use paired with Service Dogs (SD). A qualitative approach to analysis using zooeyia was applied to data collected during an exploratory patient-oriented, time-series research design with Veterans teamed with SDs through a national holistic Canadian training program. All four components of zooeyia were present in the experiences of Veterans with SDs; SDs were builders of social capital, agents of harm reduction, motivators for health behavior change, and active participants in treatment plans. While Veterans working with SDs reported many benefits, the pairs also experienced specific complex challenges, beyond the expected concerns for a household pet. The human-animal relationship between Veterans in this study and their SDs, and the subsequent growing bond, is a key contributing component and step to the strengthening of zooeyia. This analysis of zooeyia extends our understanding of how SDs support veterans’ health, including better management of PTSD and problematic substance use. Because the HAB is reciprocal, this analysis also challenges One Health to recognize and embrace concerns for animal welfare.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".