Curated Collections for Educators: Five Key Papers on Clinical Teaching
Bibliographic record
Abstract
The ability to teach in the clinical setting is of paramount importance. Clinical teaching is at the heart of medical education, irrespective of the learner’s level of training. Learners desire and need effective, competent, and thoughtful clinical teaching from their instructors. However, many clinician-educators lack formal training on this important skill and thus may provide a variable experience to their learners. Although formal training of clinician-educators is standard and required in many other countries, the United States has yet to follow suit, leaving many faculty members to fend for themselves to learn these important skills. In September 2018, the Academic Life in Emergency Medicine (ALiEM) 2018-2019 Faculty Incubator program discussed the topic of clinical teaching techniques. We gathered the titles of papers that were cited, shared, and recommended within our online discussion forum and compiled the articles pertaining to the topic of clinical teaching techniques. To augment the list, the authors did a formal literature search using the search terms “teaching techniques", "clinical teaching", "medical education", "medical students", and "residents” on Google Scholar and PubMed. Finally, we posted a call for important papers on the topic of clinical teaching techniques on Twitter. Through this process, we identified 48 core articles on the topic of clinical teaching. We conducted a modified Delphi methodology to identify the key papers on the topic. In this paper, we present the five highest-rated articles based on the relevance to junior faculty and faculty developers. This article will review and summarize the articles we found to be the most impactful to improve one’s clinical teaching skills.
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 | 0.027 | 0.201 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.099 | 0.093 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.241 | 0.073 |
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".