Exploring human-animal interactions beyond academic research: A rapid review of grey literature
Bibliographic record
Abstract
Increasing recognition of the diverse benefits of human-animal interactions (HAIs) has propelled related studies. Although most of the benefits have been illustrated by academic literature (e.g. journal articles, academic theses, and project reports), the grey literature contributes to a comprehensive understanding of HAIs and offers new perspectives, informing prospective research, practices, and policies. Adapting the Systematic Reviews and Meta-Analyses (PRISMA) approach, this rapid review examined 151 articles covering HAIs from 2016-2022. The univariate analysis results revealed that the sources covered various animal species (e.g. dogs, cats, birds), types of animals (e.g. companion animals, therapy animals, zoo animals), and vulnerable populations (e.g. older adults, people with disabilities). HAIs could be found across different settings, such as households, schools, healthcare facilities, and more. The thematic analysis identified three primary categories associated with HAIs' benefits in public education: (1) HAIs benefit the well-being of individuals, families, and animals; (2) HAIs promote prosocial behaviours and community development; and (3) HAIs improve disaster preparedness and response. The results highlight the multifaceted positive influences of HAIs on human well-being, animal welfare, and building healthy and resilient communities. Grey literature plays an essential role in knowledge mobilisation through public education, promoting the interconnectedness between human well-being and animal welfare.
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 | 0.040 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.041 | 0.030 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".