Complexities and considerations in conducting animal-assisted intervention research: A discussion of randomized controlled trials
Bibliographic record
Abstract
Abstract The field of human-animal interaction (HAI) has experienced prolific growth in the scope, breadth, and rigor of research conducted on animal-assisted interventions (AAIs). As knowledge regarding the preliminary efficacy of AAIs on outcomes of human health and wellbeing continues to accumulate, so has information regarding the feasibility, safety, and acceptability of AAIs. This progression, combined with an increase in funding opportunities, institutional resources, and growing recognition of the field from mental and medical health professionals, has led to more widespread implementation of randomized controlled trials (RCTs) in the field. While conducting RCTs in any field of study is an intensive and complex undertaking, researchers conducting RCTs to evaluate the efficacy of AAIs are faced with unique considerations. The goal of this manuscript is to discuss these complexities and considerations surrounding conducting an RCT of an AAI program in regard to study planning, conceptualization, design, implementation, and dissemination. We highlight common confounders in HAI research and provide strategies for minimizing or ameliorating them. Recommendations pertain to such unique issues as ethical considerations, theory, control and comparison groups, sampling, implementation fidelity, and transparent reporting of findings. These considerations and recommendations seek to aid HAI researchers in the design, implementation, and dissemination of future RCTs to continue to advance the rigor of the field.
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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 | 0.869 | 0.890 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.018 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.015 | 0.009 |
| Research integrity | 0.033 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".