"Galloping" towards Neurocognitive Stimulation: Equine-Assisted Therapy for Dementia in Memory Workshops
Bibliographic record
Abstract
Dementia represents a significant challenge due to its increasing prevalence and the absence of curative treatment. In this context, there is a need for interventions that provide opportunities for meaningful engagement while simultaneously promoting cognitive stimulation. Research suggests that Equine-Assisted Therapy (EAT) can enhance cognitive stimulation, promote physical activity, and improve mood through multisensory experiences (Meregillano, 2004). The main objective of this study was to investigate the impact of incorporating EAT into memory workshops on cognitive functioning, depressive symptoms, and quality of life (QoL) in individuals with mild to moderate dementia. This is a longitudinal, randomized study with 36 participants aged over 65, diagnosed with early to moderate-stage dementia, randomly divided into three groups: Equine-Assisted Therapy Workshop (EATW) (n=12), Traditional Memory Workshop (TMW) (n=12), and Control Group (CG) (n=12). The intervention conducted in the EATW and TMW groups consisted of 8 weekly sessions of 60 minutes. A pre- and post-intervention assessment of cognitive function (CF), depression levels (D), and QoL was performed using the Montreal Cognitive Assessment (MoCA), the Geriatric Depression Scale (GDS), and the Quality of Life in Alzheimer’s Disease scale for long-term care facilities (QoL-AD NH), respectively. The groups did not show statistically significant differences in terms of age, stage of dementia, or time since diagnosis. The intra-group analysis of the results demonstrates the positive impact of EATW in all dimensions, with statistically significant differences between the two assessment periods, showing improvements in CF, a decrease in D, and an increase in QoL. In contrast, participants in the TMW group showed a decrease in CF and QoL and an increase in D, with statistical significance in CF and QoL. Similarly, the CG presented a decrease in CF and QoL and an increase in D, though without statistical significance. The inter-group analysis demonstrates the positive impact of EATW, showing statistically significant differences from the other groups in all assessed dimensions. These results highlight the potential of EATW as a promising non-pharmacological therapeutic intervention for dementia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".