Preparing medical students for future learning using basic science instruction
Bibliographic record
Abstract
OBJECTIVES: The construct of 'preparation for future learning' (PFL) is understood as the ability to learn new information from available resources, relate new learning to past experiences and demonstrate innovation and flexibility in problem solving. Preparation for future learning has been proposed as a key competence of adaptive expertise. There is a need for educators to ensure that opportunities are provided for students to develop PFL ability and that assessments accurately measure the development of this form of competence. The objective of this research was to compare the relative impacts of basic science instruction and clinically focused instruction on performance on a PFL assessment (PFLA). METHODS: This study employed a 'double transfer' design. Fifty-one pre-clerkship students were randomly assigned to either basic science instruction or clinically focused instruction to learn four categories of disease. After completing an initial assessment on the learned material, all participants received clinically focused instruction for four novel diseases and completed a PFLA. The data from the initial assessment and the PFLA were submitted to independent-sample t-tests. RESULTS: Mean ± standard deviation [SD] scores on the diagnostic cases in the initial assessment were similar for participants in the basic science (0.65 ± 0.11) and clinical learning (0.62 ± 0.11) conditions. The difference was not significant (t[42] = 0.90, p = 0.37, d = 0.27). Analysis of the diagnostic cases on the PFLA revealed significantly higher mean ± SD scores for participants in the basic science learning condition (0.72 ± 0.14) compared with those in the clinical learning condition (0.63 ± 0.15) (t[42] = 2.02, p = 0.05, d = 0.62). CONCLUSIONS: Our results show that the inclusion of basic science instruction enhanced the learning of novel related content. We discuss this finding within the broader context of research on basic science instruction, development of adaptive expertise and assessment in medical education.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".