Using a conceptual framework during learning attenuates the loss of expert-type knowledge structure
Bibliographic record
Abstract
BACKGROUND: During evolution from novice to expert, knowledge structure develops into an abridged network organized around pathophysiological concepts. The objectives of this study were to examine the change in knowledge structure in medical students in one year and to investigate the association between the use of a conceptual framework (diagnostic scheme) and long-term knowledge structure. METHODS: Medical students' knowledge structure of metabolic alkalosis was studied after instruction and one year later using concept-sorting. Knowledge structure was labeled 'expert-type' if students shared >or= 2 concepts with experts and 'novice-type' if they shared < 2 concepts. Conditional logistic regression was used to study the association between short-term knowledge structure, the use of a diagnostic scheme and long-term knowledge structure. RESULTS: Thirty-four medical students completed the concept-sorting task on both occasions. Twenty-four used a diagnostic scheme for metabolic alkalosis. Short-term knowledge structure was not a correlate of long-term knowledge structure, whereas use of a diagnostic scheme was associated with increased odds of expert-type long-term knowledge structure (odds ratio 12.6 [1.4, 116.0], p = 0.02). There was an interaction between short-term knowledge structure and the use of a diagnostic scheme. In the group who did not use a diagnostic scheme the number of students changing from expert-type to novice-type was greater than vice versa (p = 0.046). There was no significant change in the group that used the diagnostic scheme (p = 0.6). CONCLUSION: The use of a diagnostic scheme by students may attenuate the loss of expert-type knowledge structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".