Using large language models for preprocessing and information extraction from unstructured text: A proof-of-concept application in the social sciences

Recent months have witnessed an increase in suggested applications for large language models (LLMs) in the social sciences. This proof-of-concept paper explores the use of LLMs to improve text quality and to extract predefined information from unstructured text. The study showcases promising results with an example focussed on historical newspapers and highlights the effectiveness of LLMs in correcting errors in the parsed text and in accurately extracting specified information. By leveraging the capabilities of LLMs in these straightforward, instruction-based tasks, this research note demonstrates their potential to improve on the efficiency and accuracy of text analysis workflows. The ongoing development of LLMs and the emergence of robust open-source options underscores their increasing accessibility for both, the quantitative and qualitative, social sciences and other disciplines working with text data

Cut off from new competition: Threat of entry and quality of primary care

We study how the threat of entry affects service quantity and quality of general practitioners (GPs). We leverage Germany’s needs-based primary care planning system, in which the likelihood of new GPs reduces by 20 percentage points when primary care coverage exceeds a cut-off. We compile novel data covering all German primary care regions and up to 30,000 GP-level observations from 2014 to 2019. Reduced threat of entry lowers patient satisfaction for incumbent GPs without nearby competitors but not in areas with competitors. We find no effects on working hours or quality measures at the regional level including hospitalizations and mortality

Change my mind: The impact of feedback in online self-assessments for study orientation on change in motivation of prospective students

High dropout rates at universities, often caused by false expectations and a lack of motivation, pose a serious problem in higher education. Online self-assessments (OSAs) assess expectations regarding a field of study (major) and provide feedback on the reality of the major, thus pointing out expectation-reality discrepancies as well as helping prospective students choose a major. Based on cognitive dissonance theory, pointing out expectation-reality discrepancies should be related to changes in motivation for the major (expectancies for success, subjective values, intention to choose a major) and this relationship should be strengthened by feedback. Past research has shown that OSAs can correct expectations and that expectation-reality discrepancies are related to motivation but has not investigated the role of feedback for this process. Therefore, we extend past research by examining whether the positive relationships between expectation-reality discrepancies and changes in motivation for a major are stronger for prospective students who receive feedback on their expectation-reality discrepancies than for prospective students who do not receive feedback after the assessment. We conducted a field experiment in which 234 prospective students were randomly assigned to one of two groups (EG1 = OSA including feedback; EG2 = OSA without feedback). As hypothesized larger expectation-reality discrepancies were associated with larger changes in motivation for a major (expectancies for success, subjective values, intention to choose a major). Beyond that, we found a moderation effect of the feedback condition showing that the positive relationships between expectation-reality discrepancies and expectancies for success were stronger when prospective students received feedback (vs. no feedback). As feedback only showed effects beyond expectation-reality discrepancies in one of the considered outcomes, both the development of assessment and feedback should be targeted to optimize the effectiveness of OSAs