Model structure for protocol adherence utilizing a manualized therapeutic massage intervention

利用标准化治疗性按摩干预措施的方案依从性模型结构

阅读:2

Abstract

Background The Protocol Training and Assessment Model (Model) was developed through collaboration between Emory University School of Medicine and Atlanta School of Massage to minimize intra- and inter-therapist variability for two research massage therapist (rMT) applied intervention arms in the Massage for Cancer-Related Fatigue (MCRF) early-phase study. The Model was followed to maintain and assess protocol integrity for the study's manualized Swedish massage therapy (SMT) and light touch (LT) interventions. Methods The Model includes initial rMT training, quarterly retraining sessions, accessible resources (scripts, treatment guides, weekly research personnel meetings), and ongoing monitoring. Model efficacy was assessed by monitoring data collected at retraining sessions, through audio recording review, and through subject and rMT reporting. Results Model application resulted in a high level of intervention consistency throughout the study. Protocol-related session comment rate by subjects was 2.7%. Few study participants reported intra-rMT or inter-rMT treatment delivery differences. Observation during retraining sessions indicated massage therapists continued to adhere to protocols. Importantly rMTs increased their participation beyond core duties, suggesting additional ways to standardize subject treatment experience. Conclusions Through systematic application of the Protocol Training and Assessment Model, continuous and collaborative quality improvement discussions between scientists and research massage therapists resulted in reliable, standardized SMT and LT interventions for the MCRF early-phase study. Future research can apply the Model to support and assess consistent rMT-delivered intervention applications.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。