Development, Implementation and Initial Evaluation of the Blueprint for MBBS Theory Exams in a Private Medical College of Pakistan
Bibliographic record
Abstract
Objective: This study aimed to enhance the validity of the exam bank at Liaquat National Medical College (LNMC), Karachi, through the development and evaluation of the exam blueprinting process as part of an ongoing quality assurance initiative. Study design and setting: This study was conducted at Liaquat National Medical College (LNMC), Karachi. Participants included key stakeholders i. faculty members, ii-officials from the examination department (involved in the development of the fourth-year MBBS neuroscience exam blueprint), and iii-students. Methodology: Ethical approval for this study was taken by the LNMC Ethics Review Committee. For the ease of understanding, this article was divided into two sections: In first section, the stepwise approach of blueprint development was discussed whereas the second section dealt with feedback from 105 Year 4 MBBS students, feedback from faculty involved in this process and lastly, the experiences of examination unit personnel were explored. Results: Following the Calgary model by Coderre et al., blueprint for undergraduate MBBS theory exam was developed. Students (85%) agreed that the exam accurately assessed the taught content. Faculty expressed satisfaction with the blueprinting process, noting improvements in exam quality, topic representation, and the elimination of redundant questions. Examination unit personnel reported better time management and improved alignment with curricular objectives. Initial challenges, such as faculty’s lack of training and resistance were also identified. Conclusion: The blueprinting process significantly enhanced alignment of theory exam with educational objectives thereby ensuring the content validity. Continued training and institutional support are vital to overcoming initial challenges
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".