Level of integration in current undergraduate curricula of two private-sector medical colleges in Karachi
Bibliographic record
Abstract
Background: Diverse strategies are employed globally to integrate medical curricula. Nevertheless, a gap exists in assessing the role of medical instructors in meaningful integration. We developed and used a tool to explore the current level of integration, score medical instructors' individual practices for integration, and investigate contextual elements minimizing integration. Methodology: = 107). We validated a paper-based questionnaire through a pilot study on five participants. This tool with 11 close-ended questions on a 5-point Likert scale generated instructors' integration scores, and six open-ended questions probed instructors' perspectives. Results: The mean integration score was 37.4±6.7. Participants' perspectives indicated a need for participation of clinical faculty in teaching initial undergraduate years, involving lecturers in curriculum meetings, and integration of assessment. The questionnaire Cronbach-alpha was 0.732 with satisfactory principal-component-analysis. Conclusion: Medical instructors facilitated integration mainly through concurrent timetabling of similar topics. Moreover, formal consultation through committee meetings, with discipline-based and integrated approaches complementing each other, were in practice to achieve curricular goals.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".