Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Clinical Social Workers' Perceptions of Large Language Models in Practice: Resistance to Automation and Prospects for Integration. |
| Συγγραφείς: |
Báez, Johanna Creswell, Ahn, Eunhye, Tamietti, Aubrey, Victor, Bryan G., Goldkind, Lauri |
| Πηγή: |
Journal of Evidence-Based Social Work (2640-8066); Jan/Feb2026, Vol. 23 Issue 1, p42-63, 22p |
| Θεματικοί όροι: |
Generative artificial intelligence, Empathy, Social workers, Professional practice, Qualitative research, Professional ethics, Social services, Interviewing, Privacy, Social worker attitudes, Natural language processing, Decision making, Judgment sampling, Thematic analysis, Research methodology, Automation, Medical ethics, Medical practice |
| Γεωγραφικοί όροι: |
United States |
| Περίληψη: |
Purpose: This research explores clinical social workers' perceptions of the usefulness of generative artificial intelligence (AI) in clinical practice, with a particular focus on large language models (LLMs). Materials and Methods: This qualitative reflexive thematic analysis explored the interviews of 21 clinical social workers and how they experience their work in the context of growing LLM use. Participants shared their perceptions and experiences with LLMs following a collaborative case consultation exercise using ChatGPT and a video demonstration of a client using ChatGPT. Results: Social work practitioners described both benefits and concerns with LLM use in their practice. Two overarching themes emerged: (1) factors that enhanced social workers' perceived usefulness of LLMs in clinical practice, including support for administrative tasks and client engagement, and (2) factors that diminished perceived usefulness, such as concerns about confidentiality, loss of nuance, and limitations in conveying empathy and contextual understanding. Discussion: Practitioners shared that they are using LLMs as idea generators in clinical work, while simultaneously expressing concern about the quality of information and the need for a human‑centered approach. They also noted that their decision to adopt LLMs is shaped by professional ethics and relational values, reflecting a preference for augmentation rather than full automation to preserve therapeutic depth and client wellbeing. Conclusion: Future AI implementation should focus on practitioner training and clear ethical guidelines to support responsible integration of LLMs. Ongoing evaluation will be essential to ensure these tools enhance clinical practice without compromising the therapeutic relationship or core social work values. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Evidence-Based Social Work (2640-8066) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Βάση Δεδομένων: |
Complementary Index |