Objective: To explore predictors of dropout of patients with chronic musculoskeletal pain from an interdisciplinary chronic pain management programme, and to develop and validate a multivariable prediction model, based on the Extended Common- Sense Model of Self-Regulation (E-CSM). Methods: In this prospective cohort study consecutive patients with chronic pain were recruited and followed up (July 2013 to May 2015). Possible associations between predictors and dropout were explored by univariate logistic regression analyses. Subsequently, multiple logistic regression analyses were executed to determine the model that best predicted dropout. Results: Of 188 patients who initiated treatment, 35 (19%) were classified as dropouts. The mean age of the dropout group was 47.9 years (standard deviation 9.9). Based on the univariate logistic regression analyses 7 predictors of the 18 potential predictors for dropout were eligible for entry into the multiple logistic regression analyses. Finally, only pain catastrophizing was identified as a significant predictor. Conclusion: Patients with chronic pain who catastrophize were more prone to dropout from this chronic pain management programme. However, due to the exploratory nature of this study no firm conclusions can be drawn about the predictive value of the E-CSM of Self-Regulation for dropout.
Objective: Systematic review to identify predictors for dropout during interdisciplinary pain management programmes. Data sources: PubMed, PsycINFO, CINAHL, Embase, and SPORTDiscus were searched from inception to 22 June 2017. Study selection: Screening, data-extraction and quality assessment was carried out independently by 2 researchers. Data synthesis: Eight studies with low methodological quality were included in this review. Out of 63 potential predictors identified in univariate analyses, significant results were found for 18 predictors of dropout in multiple logistic regression analyses in 4 domains, as described by Meichenbaum & Turk: (i) sociodemographic domain (2); (ii) patient domain (8); (iii) disease domain (6); and (iv) treatment domain (2). Conclusion: This systematic review presents an overview of predictors of dropout. The literature with regard to the prediction of dropout has focused mainly on patient characteristics and is still in the stage of model development. Future research should focus on therapist/therapy-related predictors and the interaction between these predictors. This review suggests future research on this topic, in order to generate better outcomes in interdisciplinary pain management programmes.
This paper reports on CATS (2006-2007), a project initiated by the Research Centre Teaching in Multicultural Schools, that addresses language related dropout problems of both native and non-native speakers of Dutch in higher education. The projects main objective is to develop a model for the redesign of the curriculum so as to optimize the development of academic and professional language skills. Key pedagogic strategies are the raising of awareness of personal proficiency levels through diagnostic testing, definition of linguistic demands of curriculum tasks, empowerment of student autonomy and peer feedback procedures. More specifically, this paper deals with two key areas of the project. First, it describes the design and development of web-based corpus software tools, aimed at the enhancement of the autonomy of students academic reading and writing skills. Secondly, it describes the design of three pilots, in which the process of a content and language integrated approach - facilitated by the developed web tools - was applied, and these pilots respective evaluations. The paper concludes with a reflection on the project development and the experiences with the pilot implementations.
