Perception of Indian medical students on teaching–learning in vernacular language: A qualitative survey
Bibliographic record
Abstract
Background Indian medical students from vernacular language mediums of education may face challenges in understanding the medical syllabus as modern medicine is taught exclusively in English. We investigated the perception of Indian first-year medical students regarding learning in a vernacular language. Methods We did an in-depth telephone interview with first-year medical students from various states in India. A pre-designed survey guide was used to collect data on six domains (books, classes, classroom communication, written examination, and viva) with open-ended questions. Recorded phone calls were transcribed and analyzed thematically in QDA Miner Lite v.2.0.8 (Provalis Research, Montreal, Canada). Results Eighty first-year medical students (53 male; 61 from government-run colleges; 47 had a vernacular language medium of education in high school) participated in this survey. A total of 9 themes were generated from the text transcript. The themes centered on the perception of vernacular language in books, classes, classroom questioning, expression, written examinations, attending viva voce, the option of career progression, communicating with teachers and patients, the optional nature of vernacular language, and issues related to access to online study materials. Conclusion The introduction of medical education in vernacular languages has both advantages and disadvantages. The major perceived advantages include a better understanding, improved memory, and enhanced ability to express both verbally and in written form. In contrast, the lack of books and other media in vernacular languages, inter-state migration of students, and challenges in super-specialty education are among the limitations. Hence, the implementation should be planned with caution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".