Jahandideh Sepideh, Jahandideh Mina, Barzegari Ebrahim
School of Human Services and Social Work, Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Queensland, Australia.
Department of Mathematics, Faculty of Science, Zanjan University, Zanjan, Iran.
J Clin Psychol Med Settings. 2021 Dec;28(4):798-807. doi: 10.1007/s10880-021-09771-7. Epub 2021 Mar 15.
Motivation is an important factor in encouraging individuals to attend rehabilitation and underpins many approaches to engagement. The aims of this study were to develop an accurate model able to predict individual intention to engage in outpatient cardiac rehabilitation (CR) programs based on the first stage of the Model of Therapeutic Engagement integrated into a socio-environmental context. The cross-sectional study in the cardiology ward of an Australian hospital included a total of 217 individuals referred to outpatient CR. Through an ordinal logistic regression, the effect of random forest (RF)-selected profile features on individual intention to engage in outpatient CR was explored. The RF based on the conditional inference trees predicted the intention to engage in outpatient CR with high accuracy. The findings highlighted the significant roles of individuals' 'willingness to consider the treatment', 'perceived self-efficacy' and 'perceived need for rehabilitation' in their intention, while the involvement of 'barriers to engagement' and 'demographic and medical factors' was not evident.
动机是鼓励个体参加康复治疗的一个重要因素，也是许多参与方法的基础。本研究的目的是基于融入社会环境背景的治疗参与模型的第一阶段，开发一个能够预测个体参与门诊心脏康复（CR）项目意愿的准确模型。在澳大利亚一家医院的心脏病病房进行的横断面研究共纳入了217名被转诊至门诊CR的个体。通过有序逻辑回归，探讨了随机森林（RF）选择的概况特征对个体参与门诊CR意愿的影响。基于条件推断树的RF对参与门诊CR的意愿进行了高精度预测。研究结果突出了个体“考虑接受治疗的意愿”“感知自我效能”和“感知康复需求”在其意愿中的重要作用，而“参与障碍”和“人口统计学及医学因素”的影响并不明显。
