Enhancing animal-assisted therapy: Leveraging insights into animal-induced emotions for improved patient outcomes
Bibliographic record
Abstract
In this research, we proposed that the effectiveness of personalised animal-assisted therapy could be enhanced by selecting specific animal species and breeds tailored to each patient’s emotional responses. To our knowledge, this study is the first to employ the FaceReader technique in refining animal-assisted therapy practices, potentially serving as a foundational step in minimising or alleviating stress for patients interacting with animals. We evaluated images of various animal species – including dogs (Canis familiaris), cats (Felis silvestris catus), pigs (Sus scrofa domesticus), sheep (Ovis aries), and horses (Equus caballus) – along with their respective breeds. The dog breeds assessed included Australian Shepherd, Pug, Labrador Retriever, Doberman, Miniature Schnauzer, Beagle, various mixed breeds, Yorkshire Terrier, Cane Corso, Samoyed, and Chihuahua. The cat breeds included mixed breeds, British Shorthair, Himalayan, Siamese, Sphynx, and Bengal. For horses, we examined the Norwegian Fjord, Exmoor Pony, Andalusian, and Friesian breeds, while the pigs included Vietnamese Pot-Bellied and Kunekune. Finally, the sheep breeds assessed were Herdwick and Suffolk. We utilised FaceReader-6 software to analyse the facial expressions of participants. To evaluate the acceptability of different animal species and breeds, we employed a 10-point Likert scale, ranging from 0 (extremely disliked) to 10 (extremely liked). The data were organised and analysed using Microsoft Excel and SPSS Version 24 software. A total of 50 adult participants were tested to examine the emotional responses triggered by various animal species and breeds. To assess the effects of these animals on induced emotions, we conducted a multivariate ANOVA. Additionally, Pearson correlations (r) were calculated to explore the relationship between the intensity of emotions experienced by each participant and their corresponding Likert scale ratings. The findings of this study indicated that the species of the animal significantly influences the intensity of the emotions categorised as ‘neutral’ and ‘happy,’ as well as overall valence. Moreover, the breed of the animal notably affects the intensity and valence of the ‘happy’ emotion. Ultimately, the results of this research could serve as a foundation for personalised strategies to enhance animal-assisted therapy and assist individuals in choosing a suitable pet.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".