Twelve tips for teaching in a provincially distributed medical education program
Bibliographic record
Abstract
BACKGROUND: As distributed undergraduate and postgraduate medical education becomes more common, the challenges with the teaching and learning process also increase. AIM: To collaboratively engage front line teachers in improving teaching in a distributed medical program. METHOD: We recently conducted a contest on teaching tips in a provincially distributed medical education program and received entries from faculty and resident teachers. RESULTS: Tips that are helpful for teaching around clinical cases at distributed teaching sites include: ask "what if" questions to maximize clinical teaching opportunities, try the 5-min short snapper, multitask to allow direct observation, create dedicated time for feedback, there are really no stupid questions, and work with heterogeneous group of learners. Tips that are helpful for multi-site classroom teaching include: promote teacher-learner connectivity, optimize the long distance working relationship, use the reality television show model to maximize retention and captivate learners, include less teaching content if possible, tell learners what you are teaching and make it relevant and turn on the technology tap to fill the knowledge gap. CONCLUSION: Overall, the above-mentioned tips offered by front line teachers can be helpful in distributed medical education.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".