Who is Interested in Animal-Assisted Therapy (AAT)? Features of Future Psychotherapists and Psychologists
Bibliographic record
Abstract
Abstract Previous research has established benefits to incorporating AAT in psychotherapy (e.g., Chandler, 2012 ). A growing interest in animal-assisted therapy (AAT) among mental healthcare practitioners ( Rossetti & King, 2010 ) warrants a deeper investigation into the features of future psychotherapists who are interested in AAT and how they differ from those who are not interested in AAT. Responses were obtained from 224 counselling and clinical psychology graduate students from across Canada (mean age 29.9; 88% females). The online survey revealed that participants with higher motivation to use AAT were more likely to be female, older, counselling students, live with companion animals, and have experience with AAT. Participants recognized many advantages of using AAT, such as improving health and reducing stress in clients. However, participants also identified important barriers to using AAT, such as clients’ allergies or fears of animals. Exploring the profiles of those interested in AAT, what interests them and how they perceive AAT, can help AAT training programs attract potential clients and tailor materials appropriately. Furthermore, it can increase future psychotherapists’ awareness of this therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".