The Contextual Curriculum: Learning in the Matrix, Learning From the Matrix
Bibliographic record
Abstract
Changes in the health care landscape over the last 25 years have led to an expansion of training sites beyond the traditional academic health sciences center. The resulting contextual diversity in contemporary medical education affords new opportunities to consider the influence of contextual variation on learning. The authors describe how different contextual patterns in clinical learning environments-patients, clinical and educational practices, physical geography, health care systems, and culture-form a contextual learning matrix. Learners' participation in this contextual matrix shapes what and how they learn, and who they might become as physicians.Although competent performance is critically dependent on context, this dependence may not be actively considered or shaped by medical educators. Moreover, learners' inability to recognize the educational affordances of different contexts may mean that they miss critical learning opportunities, which in turn may affect patient care, particularly in the unavoidable times of transition that characterize a professional career. Learners therefore need support in recognizing the variability of learning opportunities afforded by different training contexts. The authors set out the concept of the contextual curriculum in medical education as that which is learned both intentionally and unintentionally from the settings in which learning takes place. Further, the authors consider strategies for medical educators through which the contextual curriculum can be made apparent and tangible to learners as they navigate a professional trajectory where their environments are not fixed but fluid and where change is a constant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".