The effect of service dogs on veterans’ romantic partners and relationships: A preliminary mixed-methods study
Bibliographic record
Abstract
Abstract Romantic partners are often key supports for veterans diagnosed with posttraumatic stress disorder (PTSD). However, a PTSD diagnosis among veterans is often associated with lower relationship satisfaction and quality. To help reduce their PTSD symptomology, some veterans are more regularly relying on the help of service dogs (SDs). Relatively little is known about how SDs affect romantic relationships and veterans’ romantic partners. The current mixed-methods preliminary study explored the psycho-social effects of SDs on veterans’ romantic relationships and their partners from the romantic partners’ perspective. Romantic partners of veterans ( N = 27) completed the Partnership Questionnaire and seven partners completed interviews. Scale results indicated overall low relationship quality, with relatively lower scores for couples’ tenderness/intimacy and togetherness/communication, and higher scores for their quarreling. Interview results indicated there were perceived relationship problems due to the veterans’ PTSD, but there were some relationship improvements since the SD had been supporting the veteran, including decreased quarreling and increased tenderness/intimacy and togetherness/communication. Romantic partners reported experiencing less resentment and stress, and increased relaxation, calmness, patience, happiness, and hope for the future because of the SDs. This study suggests that SDs can influence other aspects of veterans’ lives beyond their disability, such as helping veterans reconnect with their romantic partners.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.005 | 0.001 |
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".