How to Maximize Bedside Teaching in Our Busy World
Bibliographic record
Abstract
Summary Bedside teaching is becoming less frequent. A lack of attending physicians’ time and perceived teaching skill, as well as concerns regarding the impact of bedside teaching on the relationship with patients have been cited as barriers to bedside teaching. The purpose of this paper is to offer some tips on how to increase the frequency and quality of bedside teaching in light of these barriers. The main recommendations are to 1) be explicit about the competencies around which you are teaching; 2) incorporate bedside teaching into your daily workflow, allowing the available cases and patients to dictate the learning competencies; and 3) use a framework that incorporates published teaching tools to guide your bedside teaching. The first step of this framework is preparation, which involves choosing the most appropriate teaching competency (history-taking, physical exam, clinical reasoning, or decision-making) based on the learner, case, and patient. Next is delivery, including orienting learners and patients to the task, choosing the instructional modality that fits the competency (such as One Minute Preceptor, SNAPPS, or Mini-CEX), and then debriefing and providing feedback. The final step is reflection on the teaching session, which can include peer observation.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".