Maha Gamal Ramadan Asal, Samira Ahmed Alsenany, Talal Emad Ahmed Badoman, Ahmed Abdelwahab Ibrahim El-Sayed
{"title":"使用大型语言模型的伦理意识：医疗保健专业人员量表的开发和验证","authors":"Maha Gamal Ramadan Asal, Samira Ahmed Alsenany, Talal Emad Ahmed Badoman, Ahmed Abdelwahab Ibrahim El-Sayed","doi":"10.1111/jep.70241","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>The integration of large language models (LLMs) into healthcare offers transformative potential but raises significant ethical challenges. Addressing these challenges requires a comprehensive framework to assess healthcare professionals' ethical awareness of LLMs usage.</p>\n </section>\n \n <section>\n \n <h3> Objective</h3>\n \n <p>To develop and validate a scale designed to evaluate healthcare professionals' ethical awareness regarding the integration of LLMs into clinical practice.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>This two-phase methodological study was conducted in 2024 across nine healthcare institutions—five in Egypt and four in Saudi Arabia. In Phase I, a comprehensive literature review and semi-structured interviews with healthcare professionals guided the development of the initial scale and item pool. In Phase II, the psychometric properties of the scale were evaluated using data collected from 658 healthcare professionals. Construct validity was assessed through exploratory and confirmatory factor analyses, while internal consistency reliability was examined using Cronbach's alpha (<i>α</i>) coefficients and item–total correlation metrics.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>An initial pool of 36 items was refined to 21 items across 6 dimensions: data privacy and confidentiality, consent and autonomy, transparency and accountability, bias and equity, safety and professional integrity, and education and sustainability. EFA identified a six-factor structure accounting for 71.5% of the variance. CFA confirmed the scale's structure with strong model fit indices for first-order analysis (CMIN/DF = 1.798, CFI = 0.967, RMSEA = 0.050) and second-order analysis (CMIN/DF = 2.862, CFI = 0.927, RMSEA = 0.058). The scale demonstrated excellent internal consistency (overall Cronbach's <i>α</i> = 0.90; dimensions ranging from 0.780 to 0.964) and achieved satisfactory composite reliability, convergent validity and discriminant validity. Moderate statistically significant inter-factor correlations confirmed the multidimensional nature of the scale.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The developed scale is a valid and reliable tool for assessing healthcare professionals' ethical awareness in the use of LLMs in healthcare. It provides a comprehensive framework for evaluating and enhancing ethical considerations, promoting the responsible and informed use of LLMs technologies in clinical practice.</p>\n </section>\n </div>","PeriodicalId":15997,"journal":{"name":"Journal of evaluation in clinical practice","volume":"31 5","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ethical Awareness in the Use of Large Language Models: Development and Validation of a Scale for Healthcare Professionals\",\"authors\":\"Maha Gamal Ramadan Asal, Samira Ahmed Alsenany, Talal Emad Ahmed Badoman, Ahmed Abdelwahab Ibrahim El-Sayed\",\"doi\":\"10.1111/jep.70241\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>The integration of large language models (LLMs) into healthcare offers transformative potential but raises significant ethical challenges. Addressing these challenges requires a comprehensive framework to assess healthcare professionals' ethical awareness of LLMs usage.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>To develop and validate a scale designed to evaluate healthcare professionals' ethical awareness regarding the integration of LLMs into clinical practice.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>This two-phase methodological study was conducted in 2024 across nine healthcare institutions—five in Egypt and four in Saudi Arabia. In Phase I, a comprehensive literature review and semi-structured interviews with healthcare professionals guided the development of the initial scale and item pool. In Phase II, the psychometric properties of the scale were evaluated using data collected from 658 healthcare professionals. Construct validity was assessed through exploratory and confirmatory factor analyses, while internal consistency reliability was examined using Cronbach's alpha (<i>α</i>) coefficients and item–total correlation metrics.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>An initial pool of 36 items was refined to 21 items across 6 dimensions: data privacy and confidentiality, consent and autonomy, transparency and accountability, bias and equity, safety and professional integrity, and education and sustainability. EFA identified a six-factor structure accounting for 71.5% of the variance. CFA confirmed the scale's structure with strong model fit indices for first-order analysis (CMIN/DF = 1.798, CFI = 0.967, RMSEA = 0.050) and second-order analysis (CMIN/DF = 2.862, CFI = 0.927, RMSEA = 0.058). 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Ethical Awareness in the Use of Large Language Models: Development and Validation of a Scale for Healthcare Professionals
Background
The integration of large language models (LLMs) into healthcare offers transformative potential but raises significant ethical challenges. Addressing these challenges requires a comprehensive framework to assess healthcare professionals' ethical awareness of LLMs usage.
Objective
To develop and validate a scale designed to evaluate healthcare professionals' ethical awareness regarding the integration of LLMs into clinical practice.
Methods
This two-phase methodological study was conducted in 2024 across nine healthcare institutions—five in Egypt and four in Saudi Arabia. In Phase I, a comprehensive literature review and semi-structured interviews with healthcare professionals guided the development of the initial scale and item pool. In Phase II, the psychometric properties of the scale were evaluated using data collected from 658 healthcare professionals. Construct validity was assessed through exploratory and confirmatory factor analyses, while internal consistency reliability was examined using Cronbach's alpha (α) coefficients and item–total correlation metrics.
Results
An initial pool of 36 items was refined to 21 items across 6 dimensions: data privacy and confidentiality, consent and autonomy, transparency and accountability, bias and equity, safety and professional integrity, and education and sustainability. EFA identified a six-factor structure accounting for 71.5% of the variance. CFA confirmed the scale's structure with strong model fit indices for first-order analysis (CMIN/DF = 1.798, CFI = 0.967, RMSEA = 0.050) and second-order analysis (CMIN/DF = 2.862, CFI = 0.927, RMSEA = 0.058). The scale demonstrated excellent internal consistency (overall Cronbach's α = 0.90; dimensions ranging from 0.780 to 0.964) and achieved satisfactory composite reliability, convergent validity and discriminant validity. Moderate statistically significant inter-factor correlations confirmed the multidimensional nature of the scale.
Conclusion
The developed scale is a valid and reliable tool for assessing healthcare professionals' ethical awareness in the use of LLMs in healthcare. It provides a comprehensive framework for evaluating and enhancing ethical considerations, promoting the responsible and informed use of LLMs technologies in clinical practice.
期刊介绍：
The Journal of Evaluation in Clinical Practice aims to promote the evaluation and development of clinical practice across medicine, nursing and the allied health professions. All aspects of health services research and public health policy analysis and debate are of interest to the Journal whether studied from a population-based or individual patient-centred perspective. Of particular interest to the Journal are submissions on all aspects of clinical effectiveness and efficiency including evidence-based medicine, clinical practice guidelines, clinical decision making, clinical services organisation, implementation and delivery, health economic evaluation, health process and outcome measurement and new or improved methods (conceptual and statistical) for systematic inquiry into clinical practice. Papers may take a classical quantitative or qualitative approach to investigation (or may utilise both techniques) or may take the form of learned essays, structured/systematic reviews and critiques.
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