Analysing the concept of context in medical education
Bibliographic record
Abstract
BACKGROUND: There is increasing interest in the role of context in medical education, with the conjecture that learning in a clinical context may be helpful for later recall of knowledge. Although this may be true in a general sense, at a closer look it appears that the notion of context is not well substantiated in the medical education literature and that the concept is not clearly defined. Effects of context on learning appear to depend on type of learning task, the relationship or interaction between the context and the learning material, and motivational features of the context. Context is often implicitly regarded as a uniform concept but conceptual analysis shows that a distinction can be made in several dimensions. RESULTS: In this paper, we identify 3 different dimensions of context: a physical dimension, representing the environmental characteristics; a semantic dimension, reflecting how well the context contributes to the learning task, and a commitment dimension, representing the amount of commitment (in terms of motivation and responsibility) that is generated by the context. On these dimensions, context can be ordered from reduced (providing few cues, little meaning, little commitment) to enriched (many cues, much meaning, high commitment). CONCLUSION: This model can serve a dual purpose: first, to disentangle several aspects of educational contexts (e.g. as high in meaning but low in commitment), and second, to provide a theoretical framework to generate research on the influence of different contexts in education on students' learning.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".