School of Clinical Sciences - Te Kura Mātai Haumanu

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https://hdl.handle.net/10292/1241
The School of Clinical Sciences plays an important role in specialist teaching and research conducted by its academic staff and postgraduate students. This places AUT students at the forefront of much of the ground-breaking research undertaken in New Zealand, especially in the fields of Midwifery, Nursing, Occupational Therapy, Oral Health, Paramedicine, Physiotherapy, Podiatry.

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Browsing School of Clinical Sciences - Te Kura Mātai Haumanu by Subject "08 Information and Computing Sciences"

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    ChatGPT for Automated Qualitative Research: Content Analysis
    (JMIR Publications Inc., 2024-07-25) Bijker, R; Merkouris, SS; Dowling, NA; Rodda, SN
    Background: Data analysis approaches such as qualitative content analysis are notoriously time and labor intensive because of the time to detect, assess, and code a large amount of data. Tools such as ChatGPT may have tremendous potential in automating at least some of the analysis. Objective: The aim of this study was to explore the utility of ChatGPT in conducting qualitative content analysis through the analysis of forum posts from people sharing their experiences on reducing their sugar consumption. Methods: Inductive and deductive content analysis were performed on 537 forum posts to detect mechanisms of behavior change. Thorough prompt engineering provided appropriate instructions for ChatGPT to execute data analysis tasks. Data identification involved extracting change mechanisms from a subset of forum posts. The precision of the extracted data was assessed through comparison with human coding. On the basis of the identified change mechanisms, coding schemes were developed with ChatGPT using data-driven (inductive) and theory-driven (deductive) content analysis approaches. The deductive approach was informed by the Theoretical Domains Framework using both an unconstrained coding scheme and a structured coding matrix. In total, 10 coding schemes were created from a subset of data and then applied to the full data set in 10 new conversations, resulting in 100 conversations each for inductive and unconstrained deductive analysis. A total of 10 further conversations coded the full data set into the structured coding matrix. Intercoder agreement was evaluated across and within coding schemes. ChatGPT output was also evaluated by the researchers to assess whether it reflected prompt instructions. Results: The precision of detecting change mechanisms in the data subset ranged from 66% to 88%. Overall κ scores for intercoder agreement ranged from 0.72 to 0.82 across inductive coding schemes and from 0.58 to 0.73 across unconstrained coding schemes and structured coding matrix. Coding into the best-performing coding scheme resulted in category-specific κ scores ranging from 0.67 to 0.95 for the inductive approach and from 0.13 to 0.87 for the deductive approaches. ChatGPT largely followed prompt instructions in producing a description of each coding scheme, although the wording for the inductively developed coding schemes was lengthier than specified. Conclusions: ChatGPT appears fairly reliable in assisting with qualitative analysis. ChatGPT performed better in developing an inductive coding scheme that emerged from the data than adapting an existing framework into an unconstrained coding scheme or coding directly into a structured matrix. The potential for ChatGPT to act as a second coder also appears promising, with almost perfect agreement in at least 1 coding scheme. The findings suggest that ChatGPT could prove useful as a tool to assist in each phase of qualitative content analysis, but multiple iterations are required to determine the reliability of each stage of analysis.
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    Comparison of YouthCHAT, an Electronic Composite Psychosocial Screener, With a Clinician Interview Assessment for Young People: Randomized Controlled Trial
    (JMIR Publications Inc., 2019-12-03) Thabrew, H; D'Silva, S; Darragh, M; Goldfinch, M; Meads, J; Goodyear-Smith, F
    Background: Psychosocial problems such as depression, anxiety, and substance abuse are common and burdensome in young people. In New Zealand, screening for such problems is undertaken routinely only with year 9 students in low-decile schools and opportunistically in pediatric settings using a nonvalidated and time-consuming clinician-administered Home, Education, Eating, Activities, Drugs and Alcohol, Sexuality, Suicide and Depression, Safety (HEEADSSS) interview. The Youth version, Case-finding and Help Assessment Tool (YouthCHAT) is a relatively new, locally developed, electronic tablet-based composite screener for identifying similar psychosocial issues to HEEADSSS Objective: This study aimed to compare the performance and acceptability of YouthCHAT with face-to-face HEEADSSS assessment among 13-year-old high school students. Methods: A counterbalanced randomized trial of YouthCHAT screening either before or after face-to-face HEEADSSS assessment was undertaken with 129 13-year-old New Zealand high school students of predominantly M ori and Pacific Island ethnicity. Main outcome measures were comparability of YouthCHAT and HEEADSSS completion times, detection rates, and acceptability to students and school nurses. Results: YouthCHAT screening was more than twice as fast as HEEADSSS assessment (mean 8.57 min vs mean 17.22 min; mean difference 8 min 25 seconds [range 6 min 20 seconds to 11 min 10 seconds]; P<.01) and detected more issues overall on comparable domains. For substance misuse and problems at home, both instruments were roughly comparable. YouthCHAT detected significantly more problems with eating or body image perception (70/110, 63.6% vs 25/110, 22.7%; P<.01), sexual health (24/110, 21.8% vs 10/110, 9.1%; P=.01), safety (65/110, 59.1% vs 17/110, 15.5%; P<.01), and physical inactivity (43/110, 39.1% vs 21/110, 19.1%; P<.01). HEEADSSS had a greater rate of detection for a broader set of mental health issues (30/110, 27%) than YouthCHAT (11/110, 10%; P=.001), which only assessed clinically relevant anxiety and depression. Assessment order made no significant difference to the duration of assessment or to the rates of YouthCHAT-detected positive screens for anxiety and depression. There were no significant differences in student acceptability survey results between the two assessments. Nurses identified that students found YouthCHAT easy to answer and that it helped students answer face-to-face questions, especially those of a sensitive nature. Difficulties encountered with YouthCHAT included occasional Wi-Fi connectivity and student literacy issues. Conclusions: This study provides preliminary evidence regarding the shorter administration time, detection rates, and acceptability of YouthCHAT as a school-based psychosocial screener for young people. Although further research is needed to confirm its effectiveness in other age and ethnic groups, YouthCHAT shows promise for aiding earlier identification and treatment of common psychosocial problems in young people, including possible use as part of an annual, school-based, holistic health check.
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