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Developing a Referral Protocol for Community-Based Occupational Therapy Services in Taiwan: A Logistic Regression Analysis

Because resources for long-term care services are limited, timely and appropriate referral for rehabilitation services is critical for optimizing clients’ functions and successfully integrating them into the community. We investigated which client characteristics are most relevant in predicting Taiw...

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Detalles Bibliográficos
Autores principales: Mao, Hui-Fen, Chang, Ling-Hui, Tsai, Athena Yi-Jung, Huang, Wen-Ni, Wang, Jye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749289/
https://www.ncbi.nlm.nih.gov/pubmed/26863544
http://dx.doi.org/10.1371/journal.pone.0148414
Descripción
Sumario:Because resources for long-term care services are limited, timely and appropriate referral for rehabilitation services is critical for optimizing clients’ functions and successfully integrating them into the community. We investigated which client characteristics are most relevant in predicting Taiwan’s community-based occupational therapy (OT) service referral based on experts’ beliefs. Data were collected in face-to-face interviews using the Multidimensional Assessment Instrument (MDAI). Community-dwelling participants (n = 221) ≥ 18 years old who reported disabilities in the previous National Survey of Long-term Care Needs in Taiwan were enrolled. The standard for referral was the judgment and agreement of two experienced occupational therapists who reviewed the results of the MDAI. Logistic regressions and Generalized Additive Models were used for analysis. Two predictive models were proposed, one using basic activities of daily living (BADLs) and one using instrumental ADLs (IADLs). Dementia, psychiatric disorders, cognitive impairment, joint range-of-motion limitations, fear of falling, behavioral or emotional problems, expressive deficits (in the BADL-based model), and limitations in IADLs or BADLs were significantly correlated with the need for referral. Both models showed high area under the curve (AUC) values on receiver operating curve testing (AUC = 0.977 and 0.972, respectively). The probability of being referred for community OT services was calculated using the referral algorithm. The referral protocol facilitated communication between healthcare professionals to make appropriate decisions for OT referrals. The methods and findings should be useful for developing referral protocols for other long-term care services.