Bo Cao

Karisa Parkington, Bazen G. Teferra, Marianne Rouleau-Tang et al. · utoronto

Thematic analysis provides valuable insights into participants' experiences through coding and theme development, but its resource-intensive nature limits its use in large healthcare studies. Large language models (LLMs) can analyze text at scale and identify key content automatically, potentially addressing these challenges. However, their application in mental health interviews needs comparison with traditional human analysis. This study evaluates out-of-the-box and knowledge-base LLM-based thematic analysis against traditional methods using transcripts from a stress-reduction trial with healthcare workers. OpenAI's GPT-4o model was used along with the Role, Instructions, Steps, End-Goal, Narrowing (RISEN) prompt engineering framework and compared to human analysis in Dedoose. Each approach developed codes, noted saturation points, applied codes to excerpts for a subset of participants (n = 20), and synthesized data into themes. Outputs and performance metrics were compared directly. LLMs using the RISEN framework developed deductive parent codes similar to human codes, but humans excelled in inductive child code development and theme synthesis. Knowledge-based LLMs reached coding saturation with fewer transcripts (10-15) than the out-of-the-box model (15-20) and humans (90-99). The out-of-the-box LLM identified a comparable number of excerpts to human researchers, showing strong inter-rater reliability (K = 0.84), though the knowledge-based LLM produced fewer excerpts. Human excerpts were longer and involved multiple codes per excerpt, while LLMs typically applied one code. Overall, LLM-based thematic analysis proved more cost-effective but lacked the depth of human analysis. LLMs can transform qualitative analysis in mental healthcare and clinical research when combined with human oversight to balance participant perspectives and research resources.

Xiaofeng Pan, Ming Li, Jing Zhang et al.

Different from large-scale platforms such as Taobao and Amazon, CVR modeling in small-scale recommendation scenarios is more challenging due to the severe Data Distribution Fluctuation (DDF) issue. DDF prevents existing CVR models from being effective since 1) several months of data are needed to train CVR models sufficiently in small scenarios, leading to considerable distribution discrepancy between training and online serving; and 2) e-commerce promotions have significant impacts on small scenarios, leading to distribution uncertainty of the upcoming time period. In this work, we propose a novel CVR method named MetaCVR from a perspective of meta learning to address the DDF issue. Firstly, a base CVR model which consists of a Feature Representation Network (FRN) and output layers is designed and trained sufficiently with samples across months. Then we treat time periods with different data distributions as different occasions and obtain positive and negative prototypes for each occasion using the corresponding samples and the pre-trained FRN. Subsequently, a Distance Metric Network (DMN) is devised to calculate the distance metrics between each sample and all prototypes to facilitate mitigating the distribution uncertainty. At last, we develop an Ensemble Prediction Network (EPN) which incorporates the output of FRN and DMN to make the final CVR prediction. In this stage, we freeze the FRN and train the DMN and EPN with samples from recent time period, therefore effectively easing the distribution discrepancy. To the best of our knowledge, this is the first study of CVR prediction targeting the DDF issue in small-scale recommendation scenarios. Experimental results on real-world datasets validate the superiority of our MetaCVR and online A/B test also shows our model achieves impressive gains of 11.92% on PCVR and 8.64% on GMV.