Use of scheme‐based problem solving: an evaluation of the implementation and utilization of schemes in a clinical presentation curriculum
Bibliographic record
Abstract
CONTEXT: The University of Calgary has implemented a new curriculum which is organized according to 120 ways in which patients may present to a physician. Students are taught scheme-based problem solving rather than the more typical hypothetico-deductive or search and scan approach to problem resolution. OBJECTIVE: This study sought to determine the extent to which faculty and students were implementing and utilizing scheme-based problem solving. METHOD: All classes taught within the new clinical presentation curriculum were surveyed at the year end. Participants included four classes of first-year students and three classes of second-year students. Using a 5-point scale, students responded to survey items regarding scheme implementation and utilization. RESULTS: Data were analysed using MANOVA (multivariate analysis of variance) and revealed significant differences among classes in both first- and second-year students. Increments in scheme implementation and utilization by instructors and students were observed, although instructors' utilization of schemes lagged behind that of students. A levelling effect to the benefits of schemes for problem solving was also evident. First-year students reported schemes to be very useful for learning and organizing new information. CONCLUSION: Although it has taken time to implement curriculum change, the student response to schemes has been favourable. Faculty development and further generation of pictorial/spatial representations for all schemes, to ensure that all clinical presentations provide pathways that students can use for both learning and problem solving are recommended. Whether students who utilize schemes are more successful problem solvers is not yet known but will be the subject of study as soon as scheme delivery is predominant.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".