Understanding the Lived Experience of Veterans Who Work with Service Dogs
Bibliographic record
Abstract
Introduction: An increasingly common complement to PTSD treatment for veterans involves the use of a service dog (SD). There is a lack of regulation surrounding SDs in Canada, which can make it difficult to research SDs as differences in training and access can lead to different outcomes for veteran handlers, and can prevent clear insight into the effect of the SD on their lives. In this case, focusing on the lived experience of the veteran handlers can allow us to see the benefits of working with a SD, by allowing veterans to share their first-hand knowledge. Methodology: Interpretative Phenomenological Analysis (IPA), as outlined by Smith et al. (2022), was chosen as it is suitable for developing insight into the lived/living experience of the veteran handlers, allowing them to describe and make sense of their personal experiences with their SD and the overall impact on their daily lives. Results: Veterans feel very close to their SD, expressing sentiments of unconditional love and trust, and close bonds built on mutual respect and care. Although not expressly asked about in the interviews, many veterans have brought up the need for industry standardization for SDs, in terms of training and regulatory protections, across Canada, to ensure that SDs are properly trained and that public access for SDs is protected. Principal Conclusions and Implications for Field: Sharing first-hand stories of the beneficial impact of SDs on veterans’ lives contributes valuable insights for research on SDs as well as offering hope and practical insights for veterans considering a SD for themselves. These benefits, and the negative impact on veterans from the lack of standardization, points to the need for industry regulation in Canada. References: Smith, J. A., Flowers, P., & Larkin, M. (2022). Interpretative phenomenological analysis : theory, method and research (2nd ed.).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".