The Characteristics and Motivations of Human Volunteers of Animal-Assisted Interventions
Bibliographic record
Abstract
Abstract This study examined the characteristics and motivations of people who volunteer in animal-assisted interventions (AAIs) with their dog. Surveys of volunteer motivation, prosocial attitudes, altruism, empathy, personality, and the Pet Attachment Questionnaire were conducted, and demographic data were collected from AAI volunteers (AAIVs). For comparison purposes, these measures were also given to a group of people who volunteer with animals, but not in an AAI capacity (non-AAIVs). This study found both groups to be overwhelmingly female (>90%) with university-level educations. Motivated by the value of helping others, AAIVs scored higher than non-AAIVs on scales of empathy, prosocial behaviour, and altruism. AAIVs’ personality traits were primarily agreeable and less neurotic, and they scored higher and lower, respectively, on these traits compared to non-AAIVs. For the AAIVs only, the traits of agreeableness and extraversion uniquely predicted a secure (less anxious) pet attachment. However, for non-AAIVs, conscientiousness was the only dimension that predicted a secure attachment. The discussion considers the importance of empathy, a commitment to helping, and altruism as defining characteristics of AAIVs. The relationship between personality and attachment is also discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".