{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T02:19:08Z","timestamp":1788401948347,"version":"build-2803163510"},"reference-count":56,"publisher":"SAGE Publications","issue":"2-3","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Qual Health Res"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:p>Research indicates a positive correlation between residential treatment duration and residents\u2019 positive outcomes. Between 2015 and 2019, a New Zealand residential drug rehabilitation service noted a rise in premature program exits, leading to an in-depth investigation into the individual and therapeutic community factors that impact residents\u2019 completion of the 18-week program. The aim of the study was to understand how to enhance support mechanisms that promote longer treatment stays with the view to improving well-being outcomes. The authors conducted a two-phase, mixed-methods study. They applied quantitative secondary data analysis to data collected between 2015 and 2019 from 796 participants and did follow-up qualitative data collection in 2023, where 15 former residents participated in focus groups. Six were then randomly selected to participate in an in-depth interview. This article reports findings from the interviews of that study. The aims of this article are threefold. The authors introduce data from a New Zealand drug rehabilitation service as a case for using ChatGPT to support AI-assisted thematic narrative analysis. Steps in the analysis are detailed through a reproducible prompting process. Second, the authors present findings highlighting factors influencing residents to leave treatment and those that influenced them to stay. The authors position AI as a complementary tool for qualitative data analysis that enhances methodological rigor and practical applications in addiction research.<\/jats:p>","DOI":"10.1177\/10497323251367177","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T04:43:12Z","timestamp":1760071392000},"page":"231-246","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["To Leave or Stay? 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