Best Qualities of Medical Facilitator, Students Perceptive View
Bibliographic record
Abstract
Introduction: Medical education is blended day by day and there's a continuing need to assess the role of the facilitator in the field of medical education. A great medical facilitator is the one who clear the way within the making of our future clinicians. In this study we attempted to assess the qualities of best medical facilitator in basics and clinical sciences including all medical, surgical and their allied subjects from first year to final year MBBS Students Objective: To expedite the views of MBBS students at Azra Naheed Medical College about the best qualities of Medical facilitator Material and Methods Study design: quantitative cross sectional Settings: Azra Naheed Medical College Duration: Six months i.e. 1st January 2021 to 30th June 2021 Data Collection procedure: A well planned study was done at Azra Naheed medical college which includes all the MBBS students who participated after giving consent. The total numbers of participants in the study were 400. A validated questionnaire comprises of 12 leading statements regarding best qualities of medical facilitator was circulated. The collected data was analyzed by using SPSS version 23. Results: The total number of participants in the study is 400 in which 60% participants were females and 40% were males. The age group in the study is 18-24 years. In this study top five qualities of nest medical facilitator were highlighted. Conclusion: A great facilitator is somebody who is receptive, engaging and motivating, and who includes a sound knowledge of subject of what they are attempting to instruct. They too have the capacity to communicate well with students. Key words: Medical facilitator, Student, Medical, Qualities
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".