For the love of acronyms: An analysis of terminology and acronyms used in AAI research 2013–2023
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The involvement of animals to assist or facilitate activities, education, or therapy has become increasingly popular. As we recognize animals’ roles in ameliorating well-being and educational outcomes, researchers and programmers are developing a variety of animal-assisted programs. This diversification has seen the adoption of a plethora of terms and acronyms. Many researchers have pointed out this over-abundance of terms and their inconsistent use, arguing that this creates confusion within the field. The aims of this article were threefold: (1) To identify commonly used terms in animal-assisted intervention (AAI) research; (2) to document their use by frequency; and (3) discuss the benefits and obstacles of the abundance of terms and acronyms in the field. A search of peer-reviewed articles published in English from 2013 to 2023 was conducted across four databases: PsycInfo, Education Source, ERIC, and Scopus to collate articles related to human-animal interactions (HAIs). Records were de-duplicated in Covidence and screened at title/abstract level by two independent reviewers for relevance to AAIs. The resulting articles ( N = 1934) were subsequently coded to track terminology. A total of 1414 distinct terms were identified, the majority of which (77.8%, n = 1100) were used only once between 2013 and 2023. Only 48 terms (3.4%) were used in the literature more than 10 times. Analysis also provided insight into frequently used terms, the most prevalent of which were “animal-assisted therapy” (8.70%, used 376 times), “animal-assisted interventions” (7.45%, used 322 times), and “therapy dog” (5.06%, used 219 times). Trends across 10 years reveal that specific terms have increased (e.g., “animal-assisted intervention”) or decreased (e.g., “hippotherapy”) in popularity but that the average number of terms used per article remains stable. Despite calls from HAI researchers to reduce redundant terms and improve the accuracy and consistency in the language used, there remains a surplus of terms in the field. This holds implications for AAI researchers, programmers, and individuals gaining interest in AAIs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it