Artificial Intelligence is no more the talk of the fiction read in novels or seen in movies. It has been making inroads slowly and gradually in medical education and clinical management of patients apart from all other walks of life. Recently, chatbots particularly ChatGPT, were developed and trained, using a huge amount of textual data from the internet. This has made a significant impact on our approach in medical science. Though there are benefits of this new technology, a lot of caution is required for its use. doi: https://doi.org/10.12669/pjms.39.2.7653 How to cite this: Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT - Reshaping medical education and clinical management. Pak J Med Sci. 2023;39(2):---------. doi: https://doi.org/10.12669/pjms.39.2.7653 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objective:To develop a tool to evaluate faculty perceptions of assessment quality in an undergraduate medical program.Methods:The Assessment Implementation Measure (AIM) tool was developed by a mixed method approach. A preliminary questionnaire developed through literature review was submitted to a panel of 10 medical education experts for a three-round ‘Modified Delphi technique'. Panel agreement of > 75% was considered the criterion for inclusion of items in the questionnaire. Cognitive pre-testing of five faculty members was conducted. Pilot study was done with 30 randomly selected faculty members. Content validity index (CVI) was calculated for individual items (I-CVI) and composite scale (S-CVI). Cronbach's alpha was calculated to determine the internal consistency reliability of the tool.Results:The final AIM tool had 30 items after the Delphi process. S-CVI was 0.98 with the S-CVI/Avg method and 0.86 by S-CVI/UA method, suggesting good content validity. Cut-off value of < 0.9 I-CVI was taken as criterion for item deletion. Cognitive pre-testing revealed good item interpretation. Cronbach's alpha calculated for the AIM was 0.9, whereas Cronbach's alpha for the four domains ranged from 0.67 to 0.80.Conclusions:‘AIM' is a relevant and useful instrument with good content validity and reliability of results, and may be used to evaluate the teachers´ perceptions about assessment quality.
Objectives: To analyze the low to medium distractor efficiency items in a multiple-choice question (MCQ) paper for item writing flaws. Methods: This qualitative study was conducted at Islamic International Medical College Rawalpindi, in October 2019. Archived item- analysis report from a midyear medium stakes MCQ paper of 2nd year MBBS class, was analyzed to determine the non-functional distractors (NFDs) and distractor efficiency (DE) of items, in a total of 181 MCQs. DE was categorized as low (3-4 NFDs), medium (1-2 NFDs) and high (0 NFD). Subsequently, qualitative document analysis of the MCQ paper whose item analysis report was assessed was conducted to investigate the item flaws in the low to medium DE items. The flaws identified were coded and grouped as, within option flaws, alignment flaws between options and stem/ lead-in and other flaws. Results: Distractor efficiency was high in 69 items (38%), moderate in 75 items (42%) and low in 37 items (20%). The item-writing flaws identified in low to moderate DE items within distractors included, non-homogenous length (1.8%), non-homogenous content (8%) and repeat in distractor (1.7%). Alignment flaws between distractors and stem/ lead-in identified were linguistic cues (10%), logic cues (12.5%) and irrelevant distractors (16%). Flaws unrelated to distractors were low cognitive level items (40%) and unnecessarily complicated stems (11.6%). Conclusions: Analyzing the low to medium DE items for item writing flaws, provides valuable information about item writing errors which negatively impact the distractor efficiency. doi: https://doi.org/10.12669/pjms.36.5.2439 How to cite this:Sajjad M, Iltaf S, Khan RA. Nonfunctional distractor analysis: An indicator for quality of Multiple choice questions. Pak J Med Sci. 2020;36(5):---------. doi: https://doi.org/10.12669/pjms.36.5.2439 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Workplace-based learning is considered as one of the most effective way of translating medical theory into clinical practice. Although employed traditionally at postgraduate level, this strategy can be used in undergraduate students coming for clerkships in clinical departments. There are many challenges to workplace learning such as, unfavorable physical environment, lack of interest by clinical staff and teachers, and lack of student motivation. Clinical teachers can help bridge this gap and improve workplace learning through individual and collaborative team effort. Knowledge of various educational theories and principles and their application at workplace can enhance student learning and motivation, for which faculty development is much needed. Different teaching and learning activities can be used and tailored according to the clinical setting. Active reflection by students and constructive feedback from the clinicians forms the backbone of effective workplace learning.
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