Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)
Abstract Introduction There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychomet...
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BMC Medical Education |
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| Online Access: | https://doi.org/10.1186/s12909-023-04553-1 |
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| author | AmirAli Moodi Ghalibaf Maryam Moghadasin Ali Emadzadeh Haniye Mastour |
| author_facet | AmirAli Moodi Ghalibaf Maryam Moghadasin Ali Emadzadeh Haniye Mastour |
| author_sort | AmirAli Moodi Ghalibaf |
| collection | DOAJ |
| description | Abstract Introduction There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results. Methods In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance. Results Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively. Conclusions The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students’ readiness for medical artificial intelligence. |
| first_indexed | 2024-03-09T15:05:55Z |
| format | Article |
| id | doaj.art-6d2e77425e5a4fc8b8f852837de74dcb |
| institution | Directory Open Access Journal |
| issn | 1472-6920 |
| language | English |
| last_indexed | 2024-03-09T15:05:55Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Education |
| spelling | doaj.art-6d2e77425e5a4fc8b8f852837de74dcb2023-11-26T13:39:49ZengBMCBMC Medical Education1472-69202023-08-012311910.1186/s12909-023-04553-1Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)AmirAli Moodi Ghalibaf0Maryam Moghadasin1Ali Emadzadeh2Haniye Mastour3Student Research Committee, Faculty of Medicine, Birjand University of Medical SciencesDepartment of Clinical Psychology, Faculty of Psychology and Education, Kharazmi UniversityDepartment of Medical Education, Faculty of Medicine, Mashhad University of Medical SciencesDepartment of Medical Education, Faculty of Medicine, Mashhad University of Medical SciencesAbstract Introduction There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results. Methods In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance. Results Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively. Conclusions The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students’ readiness for medical artificial intelligence.https://doi.org/10.1186/s12909-023-04553-1Artificial intelligenceMedical educationMedical studentsPsychometric propertiesValidity and reliability |
| spellingShingle | AmirAli Moodi Ghalibaf Maryam Moghadasin Ali Emadzadeh Haniye Mastour Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) BMC Medical Education Artificial intelligence Medical education Medical students Psychometric properties Validity and reliability |
| title | Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) |
| title_full | Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) |
| title_fullStr | Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) |
| title_full_unstemmed | Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) |
| title_short | Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) |
| title_sort | psychometric properties of the persian version of the medical artificial intelligence readiness scale for medical students mairs ms |
| topic | Artificial intelligence Medical education Medical students Psychometric properties Validity and reliability |
| url | https://doi.org/10.1186/s12909-023-04553-1 |
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