Training paradigms to enhance clinical observational skills in clinical practice: A scoping review
Bibliographic record
Abstract
A number of training approaches to improve the clinical observation skills of undergraduate students have been identified in the literature. Immediate improvement from such approaches on students' clinical observational skills have been documented. However, this review identified that observational skill improvements did not occur in real and complex clinical conditions where the incidence of perception failure may increase. In six out of seven approaches examined, (i) the visual attention paid by students during observation is more focused than the actual visual attention clinicians usually pay in the real clinical area; (ii) the observations were made on images of clinical cases with visible signs which allowed findings to be noticed easily and with minimal searching efforts; (iii) the improvement in observation skills was based on what was noticed rather than what was missed, hence, perceptual failure was concealed; and (iv) in evaluations, students were asked to describe “what they see”, the process of describing has the possibility to increase the tendency to conflate observations with inferences, and as a result, students may have stopped searching after being satisfied with their findings. To conclude, this review showed that perception paradigms have not been acknowledged in clinical observation training approaches with a need for further research relating to visual perception in clinical settings.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".