Curricular mapping: an anti-stress tool for new medical students
Bibliographic record
Abstract
Objective: Recently, The University of Ottawa implemented an innovative curriculum for the incoming undergraduate medical class.Among many revisions, the curriculum became much more integrated and moved from 12 educational "blocks" to 6 integrative "units."While this approach to the integration of content was pedagogically robust, it proved to be extremely challenging for first-year medical students.Many students found it difficult to understand how concepts fit together.The purpose of this study was to create a tool that could map the trajectory of the curriculum in order to reduce stress among students.Method: During the summer of 2009, two students produced a "Foundations Unit Map."This map grouped lectures into seven interconnected, colorcoded disciplines.Subfields were bridged on the map by "integrative" topics that intentionally straddled more than one discipline.Result: The map was presented to the incoming class of the subsequent academic year.Some students reported that they felt less anxious about the range of topics to be covered as foundational to medicine and others found the presentation of all of the topics overwhelming, while others found that the map did not alter their stress levels.Conclusion: Curriculum maps can be effective tools for faculty and students, particularly where curricula are presented in innovative and challenging ways.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".