Targeted need’s assessment: Medical ethics in MBBS curriculum of Pakistan
Bibliographic record
Abstract
Objective: To assess the learner need’s assessment of medical ethics in undergraduate medical curriculum of Pakistan. Methods: To establish an actual need, three methods were employed during October 2018. The first included a review of the curriculum for medical ethics as designed by the Pakistan Medical and Dental College (PMDC). A supplementary document “Code of Ethics”, published by Pakistan Medical and Dental College (PMDC), was also reviewed. In the second method, a self-administered questionnaire was distributed to all 500 undergraduate medical students at Poonch Medical College. Data analysis was performed through SPSS v 23.0 (IBM Corporation, Armonk, NY, US) at 95% CI. The results were expressed in the form of frequencies. The third method employed was an extensive review of literature to identify gaps and to propose learning strategies. Results: In the section on guiding principles in the curriculum, Ethics is considered as an optional subject. Bioethics is designated to be taught in the 3rd year of the MBBS, as part of Forensic Medicine. The agreement to study Medical Ethics Principles as part of the curriculum among final-year medical students saw numbers almost double to 84.61%. The highest majority was seen among final year medical students where 84.6% of the students agreed to study principles of medical ethics as part of their curriculum. Conclusions: Data and the PMDC curriculum support the incorporation of medical ethics in undergraduate education. Thus, an effective educational program based on the assessment of needs could be developed for medical ethics. doi: https://doi.org/10.12669/pjms.35.5.873 How to cite this:Javaeed A. Targeted need’s assessment: Medical ethics in MBBS curriculum of Pakistan. Pak J Med Sci. 2019;35(5):1253-1257. doi: https://doi.org/10.12669/pjms.35.5.873 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.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".