Preparing medical students to become skilled at clinical observation
Bibliographic record
Abstract
Background: Observation is a fundamental skill for physicians and it is has been the subject of a resurgent interest. Although strategies for teaching observation have been described previously, many of them linked conceptually to emerging insights in visual literacy and aesthetic development, principles of clinical observation have not been elucidated.Aims: The purpose of this study was to develop a set of principles that would be useful in guiding educators teach medical students how to observe.Methods: The authors conducted a comprehensive review of the literature on the history and theory of clinical observation. They then consulted a group of individuals from a highly diverse background who, based on the nature of their work, were considered to have expertise in observation.Results: Informed by the literature and the group of experts, the authors developed a set of four guiding principles relating to pedagogy and eight core principles of clinical observation. In the context of curriculum renewal at the Faculty of Medicine, McGill University, these principles were then used to create specific teaching modules.Conclusions: Principles that are pragmatic in nature, anchored in a theoretical framework of visual competence and applicable to medical education have been developed and successfully deployed. Let someone say of a doctor that he really knows his physiology or anatomy, that he is dynamic–these are real compliments; but if you say he is an observer, a man who really knows how to see, this is perhaps the greatest compliment one can make. J.M. Charcot (Citation)
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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".