@article{smink_et_al_2019,
    doi = {10.1371/journal.pone.0225703},
    author = {Smink, Wouter AND Sools, Anneke M. AND van der Zwaan, Janneke M. AND Wiegersma, Sytske AND Veldkamp, Bernard P. AND Westerhof, Gerben J.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Towards text mining therapeutic change: A systematic review of text-based methods for Therapeutic Change Process Research},
    year = {2019},
    month = {12},
    volume = {14},
    url = {https://doi.org/10.1371/journal.pone.0225703},
    pages = {1-21},
    abstract = {Therapeutic Change Process Research (TCPR) connects within-therapeutic change processes to outcomes. The labour intensity of qualitative methods limit their use to small scale studies. Automated text-analyses (e.g. text mining) provide means for analysing large scale text patterns. We aimed to provide an overview of the frequently used qualitative text-based TCPR methods and assess the extent to which these methods are reliable and valid, and have potential for automation. We systematically reviewed PsycINFO, Scopus, and Web of Science to identify articles concerning change processes and text or language. We evaluated the reliability and validity based on replicability, the availability of code books, training data and inter-rater reliability, and evaluated the potential for automation based on the example- and rule-based approach. From 318 articles we identified four often used methods: Innovative Moments Coding Scheme, the Narrative Process Coding Scheme, Assimilation of Problematic Experiences Scale, and Conversation Analysis. The reliability and validity of the first three is sufficient to hold promise for automation. While some text features (content, grammar) lend themselves for automation through a rule-based approach, it should be possible to automate higher order constructs (e.g. schemas) when sufficient annotated data for an example-based approach are available.},
    number = {12},

}