Exploring Mattering and the Human-Animal Bond: The Impact of Service Dogs for Military Veterans at High Risk For Suicide
Bibliographic record
Abstract
Despite ample anecdotal evidence, there are limited meaningful studies speaking to the important role that animal-assisted intervention (AAI) may have in reducing suicide risk. However, research is increasingly showing the viability of service dogs (SDs) being used as a complementary approach for military Veterans suffering from post-traumatic stress disorder (PTSD) and substance use harms – two of the strongest indicators for suicidality across any population. Using a critical suicidology approach with a One Health framework, my Master’s research utilized the concept of zooeyia - which recognizes the health benefits of animals in the lives of humans – to explore the significant role the human-animal bond (HAB) has in meditating suicidality. Using in-depth interview data from 28 transcripts that spanned an 18-month period, I undertook a secondary thematic analysis to explore the experiences of Canadian military Veterans at high risk for suicide working with SDs. My methodological approach used emotion and pattern coding to discover how the unique social support system enabled by the SDs can act as a catalyst to increase feelings of “mattering.” Mattering is a validated construct shown to reduce feelings of depression, loneliness, and hopelessness that are commonly associated with suicidal behavior. My study is the first of its kind, known to me, to show that feelings of mattering can exist between a human and animal; this conclusion is based on the presence of the indicators of mattering appearing between all Veteran and SD pairings within the sample. Further to this, the SDs were reported by the Veterans as being the direct catalyst in reducing self-harm and suicidality, while also promoting feelings of hope for “healing.” While acknowledgement of how context specificity and the unique lived experience of each person remains crucial for making sense of suicidality, the significant finding from this research has been the uncovering of the synergistic impact that mattering has in the lives of Veterans where the SD has been a bridge to improve their overall quality of life - a finding that may be critical in helping reduce future suicide risk among military Veterans.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".