Medical Teaching Resources for Faculty Developers
Bibliographic record
Abstract
Abstract This module is a collection of 40 video vignettes developed for use by faculty developers in a variety of settings. The vignettes depict effective and ineffective teaching methods. There is an accompanying resource manual with guiding questions and suggestions for how the vignettes may be used in training. While many of the video vignettes target those who train medical faculty, others may be used by those involved in training the learners at all educational levels. Each video has been kept deliberately short so that it can be used to quickly demonstrate a technique, or as a starter for discussions. Using these, participants may be asked to critically analyze good and not-so-good ways of teaching. This DVD is divided into four major categories: presentation skills, active learning strategies, small-group teaching, and clinical teaching. Each category has been further divided into specific teaching methods. Questions added under each of the categories, may be used to actively engage participants watching the videos. This resource has been used as part of the 2-day Teaching Improvement Project Systems (TIPS) workshops to train faculty and residents at the College of Medicine, University of Saskatchewan, Canada. TIPS is mandatory for all new faculty. All residents take TIPS in their first and second year of training. During TIPS, these videos are used to trigger discussions, as well as identify effective and ineffective teaching methods.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.395 | 0.138 |
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".