Leveraging Literature for Health Education: The Ideal Teacher Concept in the Works of Khāqānī Shervānī (c. 1120–c. 1199) In Honor of Professor Mohammad Reza Rashed Mohassel's Legacy, Book Chapter Review
Bibliographic record
Abstract
Background: This article explores the integration of Persian poetry, particularly the works of 12th-century poet and physician Khāqānī Shervānī, into modern medical education to enhance communication strategies and patient care.Method: We investigated the potential benefits of using poetry to promote cultural awareness, health literacy, and effective teaching. The research emphasizes the poet's depiction of ideal teaching qualities by analyzing Khaqani's poetry through thematic, linguistic, and educational lenses. Khaqani portrays wisdom, compassion, and authority as essential qualities for educators, drawing on metaphysical, emotional, and symbolic language to highlight the roles of educators and healers in society.Results: The study's results show that Khaqani's metaphors and vivid imagery can inspire medical educators to incorporate humanistic and spiritual elements into their teaching practices. His poetry also reflects a strong awareness of health and medicine, making it a valuable resource for understanding the state of health literacy in 12th-century Persia (Iran). The article argues that incorporating Khaqani's poetry into medical curricula can help improve communication skills, enhance cultural competence, and foster critical thinking in medical students.Conclusion: This approach encourages a more holistic view of education, blending scientific knowledge with cultural and historical perspectives. Ultimately, integrating classical Persian literature into medical education can improve future healthcare professionals' intellectual and emotional development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".