Veteran Recreational Therapy Engagement Survey for Machine Learning Prediction

Data from a 2026 cross-sectional study of 57 veterans aged 18 and above at the New York State Veterans Home at Oxford. The dataset, authored by Mohammad Najeh Samara, was collected via a self-administered survey to predict recreational therapy participation using machine learning models.

Use Cases

• Predicting high recreational therapy participation based on activity preference diversity and total preferences.
• Identifying veterans at risk of non-participation based on facility tenure and limited preferences.
• Evaluating the influence of group and spiritual activities on engagement patterns.
• Developing tailored engagement strategies for new residents during the critical 1.5-year adaptation period.

Strengths

• Includes data from 57 veterans, providing a basis for initial model development.
• Survey captured demographic characteristics, participation patterns, and activity preferences.
• Machine learning models were validated using cross-validation techniques, with Random Forest achieving an F1-score of 0.860 for high participation prediction.
• Feature importance analysis identified specific predictors like activity preference diversity (Gini importance: 0.293).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The study sample of 57 veterans from a single facility limits generalizability.

Provenance

Source

New York State Veterans Home at Oxford

Collection Method

Comprehensive self-administered survey

Freshness

Last updated 2026-04-30 05:26:30; freshness should be verified.

Geography

New York State, USA