Survey of international academic centers and institutes focused on human-animal bond: Scope and landscape in 2021
Bibliographic record
Abstract
Routine surveying of academic centers focused on the human-animal bond is critical to understand the trajectory of the field and to create an environment where centers can learn from one another and build collaborations. The purpose of this manuscript was to report the findings of a survey of these human-animal bond centers, to summarize the status of the field, and to identify changes within the field since 2016. Survey questions concentrated on the demographic characteristics, engagement programs, educational opportunities, and research focuses of the centers. Findings suggest that the field continues to grow as one-third of human-animal bond centers are less than 10 years old. The number of centers that participated in this survey increased by 31% compared to the previous survey (O'Haire et al., 2018). Centers have developed a variety of engagement programs, including animal-assisted intervention and companion animal education programs. About half of the centers (48%) offer degree programs and about one quarter of the centers (24%) offer certificate programs. Most centers (95%) focus their research on companion animals with the most studied companion animal being dogs (95%). The most frequent data collection method was surveys (86%). Qualitative analyses, behavior measures, and physiological measures were also common. The most notable changes from the 2016 survey include overall growth of the field (indicated by the establishment of new centers) and a shift in the specialty area of directors, moving from 44% of directors being veterinarians in 2016 survey to 90% having a human-focused specialty in the 2021 survey. Most centers' research focused on animal-assisted interventions which is consistent with the previous survey. As the field of the human-animal bond continues to grow and more centers emerge, ongoing evaluation of offerings is important to track changes, identify needs, and foster success.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".