Lived Experiences of Educational Leaders in Iranian Medical Education System: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: High quality educational systems are necessary for sustainable development and responding to the needs of society. In the recent decades, concerns have increased on the quality of education and competency of graduates. Since graduates of medical education are directly involved with the health of society, the quality of this system is of high importance. Investigation in the lived experience of educational leaders in the medical education systems can help to promote its quality. The present research examines this issue in Iran. METHODOLOGY: The study was done using content-analysis qualitative approach and semi-structured interviews. The participants included 26 authorities including university chancellors and vice-chancellors, ministry heads and deputies, deans of medical and basic sciences departments, education expert, graduates, and students of medical fields. Sampling was done using purposive snowball method. Data were analyzed using conventional content analysis. FINDINGS: Five main categories and 14 sub-categories were extracted from data analysis including: quantity-orientation, ambiguity in the trainings, unsuitable educational environment, personalization of the educational management, and ineffective interpersonal relationship. The final theme was identified as "Education in shadow". CONCLUSION: Personalization and inclusion of personal preferences in management styles, lack of suitable grounds, ambiguity in the structure and process of education has pushed medical education toward shadows and it is not the first priority; this can lead to incompetency of medical science graduates.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".