Cause and Effect
Bibliographic record
Abstract
BACKGROUND: Methods of integrating basic science with clinical knowledge are still debated in medical training. One possibility is increasing the spatial and temporal proximity of clinical content to basic science. An alternative model argues that teaching must purposefully expose relationships between the domains. The authors compared different methods of integrating basic science: causal explanations linking basic science to clinical features, presenting both domains separately but in proximity, and simply presenting clinical features METHOD: First-year undergraduate health professions students were randomized to four conditions: (1) science-causal explanations (SC), (2) basic science before clinical concepts (BC), (3) clinical concepts before basic science (CB), and (4) clinical features list only (FL). Based on assigned conditions, participants were given explanations for four disorders in neurology or rheumatology followed by a memory quiz and diagnostic test consisting of 12 cases which were repeated after one week. RESULTS: Ninety-four participants completed the study. No difference was found on memory test performance, but on the diagnostic test, a condition by time interaction was found (F[3,88] = 3.05, P < .03, ηp = 0.10). Although all groups had similar immediate performance, the SC group had a minimal decrease in performance on delayed testing; the CB and FL groups had the greatest decreases. CONCLUSIONS: These results suggest that creating proximity between basic science and clinical concepts may not guarantee cognitive integration. Although cause-and-effect explanations may not be possible for all domains, making explicit and specific connections between domains will likely facilitate the benefits of integration for learners.
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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 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".