Bibliographic record
Abstract
India has a rich tradition of imparting education in Medicine since the ancient times; which has evolved over the years. As the years progressed several reforms in medical education have been made until recently the Competency Based Medical Curriculum (CBME) has been introduced in the medical colleges in India. Although CBME has been the framework of medical education in Western countries such as Canada, US, UK it has been adopted from 2019-20 onwards in India. This curriculum of Medical Education has goals to create “Indian Medical Graduate” (IMG) with a set of roles expected to be fulfilled in the form of achieved “competencies” with inclusion of qualities such as clinician, life-long learner, communicator, leader and medical professional. There are certain challenges outlined for the implementation of CBME. However, there are also guidelines in the form of various Teaching -Learning Methods (TLM) and appropriate Evaluation or Assessment Methods to be adopted. The types of TLM outlined are Integrated Method, Self -Directed Learning (SDL), Case Based Learning, Small Group Discussion, Flip Classroom Model, Mentor -Mentee System etc. in this paper. Assessment or Evaluation methods strongly support the backbone of the curriculum. Either Formative or Subjective Methods of Assessment can be adopted to monitor the TLM. The types of Assessment which can be applied are also reviewed. Although the scope and impact of CBME are immense but it faces the test of time in future. The current medical education is time-based, outcome -based, involves structured learning, time-flexible and learner -centric approach is utilized.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".