Distance Learning in Clinical Transplantation: A Successful Model in Post-Graduate Education
Bibliographic record
Abstract
Background and Purpose: There are misconceptions among clinicians and educational bodies that online courseswould not suit clinically orientated medical education, where bedside management and direct contact with realpatients is the key to the learning process. Whereas, the proponents of online education believe that a well-designedand properly blueprinted course can deliver the required education that would efficiently meet the expectations ofboth the student and these educational bodies. While variations in medical practice are a norm that warrant a flexibleand, at the same time, a focused approach is required to ensure that ‘threshold concepts’ are learnt, which is anadditional challenge. We aimed to facilitate the students to develop skills of learning from work-based reflectivepractice by linking with their own real-time analysis of their own clinical experience. This is supported by a robustscientific basis, a unique opportunity for many clinicians who may not have a reliable opportunity for discussion and,thereby, this course (MSc in Transplantation) provides a platform that allows everyone to learn from each other’sexperience by using ‘e-blackboard®’.Methods: Not just a knowledge transfer, the critical thresholds of each and every chapter in the 4 modules of MSc inTransplantation Sciences were defined to a razor-sharp precision. Learning objectives of learning activity wereaimed to achieve constructive alignment with critical threshold. We employed Kirkpatrick pyramid (satisfaction,learning, impact, results and return on investment) (a) for the evaluation of our performance as educators of theprogramme, and (b) to evaluate the acceptance of this non-traditional format in clinical medicine education bypostgraduate students (80 transplant clinicians from 22 countries).Results: Students’ survey of the first cohort (satisfaction, Kirkpatrick level 1) reported 93% students’ satisfactionrate. 93% of the students passed module one (learning Kirkpatrick level 2) and 100% subscription to module two(return on investment Kirkpatrick level 5).Conclusion: For a successful model in distance learning in clinical medicine it is imperative to establish an effectivesupportive contact using a range of modalities in order to allow real-time reflective practice that is so crucial inenabling the students to learn from their own clinical experience.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".