The effect of animal-assisted interventions on the course of neurological diseases: a systematic review
Bibliographic record
Abstract
BACKGROUND: In our experience, working with a therapy animal strengthens endurance, maintains motivation, provides a sense of achievement, and boosts overall mental resilience. The aims of this work were to summarize the results of quantitative research on the possibilities of animal-assisted intervention (AAI) among people with neurodegenerative and cerebrovascular diseases and to attempt to assess the effects of animal-assisted interventions in an objective manner and to find supporting evidence based on published literature. METHODS: Our target groups are people diagnosed with Parkinson's disease, multiple sclerosis, or stroke. A systematic search of relevant articles was conducted by two independent researchers in April 2021 and August 2023. The search for studies was conducted using PubMed, Google Scholar, Web of Science, Scopus, and Ovid databases, specifying keywords and search criteria. The qualitative evaluation of the research reports was conducted by four independent researchers, using the Newcastle-Ottawa Quality Assessment Form. RESULTS: According to the scientific criteria and based on the Newcastle-Ottawa Quality Assessment Form, thirteen publications met the search criteria, out of which 9 publications were rated good and 4 publications were rated poor. Evaluating the publications we found evidence that AAI had a measurable impact on participants, as their physical and mental health status significantly improved; however, mental health improvement was more prominent. CONCLUSIONS: By developing evidence-based research methodology and standardized research settings, AAI could be measured effectively as part of health care practice. This would bring significant benefits to the rehabilitation of patients in need. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021255776.
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 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".