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Progress reports and case notes contain rich information about program participants' experiences and frequently describe theoretically important risk and protective factors that are not typically recorded in administrative datasets. However, the unstructured narrative nature of these types of data - and, often, the sheer volume of it - is a barrier for human-drive qualitative analysis of this data. Often, the data cannot be included in evaluations because it is too time and resource intensive to do so.
This paper will describe three approaches to the qualitative analysis of progress reports used to examine within-program trajectories for participants, and the factors important for program success as part of an evaluation of the Queensland Drug and Alcohol Court.
It will explore how we navigated the balance between human and machine-driven qualitative analysis. We will reflect on the benefits and challenges of text-mining - how humans and machines stack up against each other when identifying the sentiment and emotion in text, the strengths and challenges of each approach, the lessons we have learned, and considerations for using these types of approaches to analyse datasets of progress reports in future evaluations.
