Embracing standardisation and contextualisation in medical education
Bibliographic record
Abstract
CONTEXT: The tensions that emerge between the universal and the local in a global world require continuous negotiation. However, in medical education, standardization and contextual diversity tend to operate as separate philosophies, with little attention to the interplay between them. METHODS: The authors synthesise the literature related to the intersections and resulting tensions between standardization and contextual diversity in medical education. In doing so, the authors analyze the interplay between these competing concepts in two domains of medical education (admissions and competency-based medical education), and provide concrete examples drawn from the literature. RESULTS: Standardization offers many rewards: its common articulations and assumptions promote patient safety, foster continuous quality improvement, and enable the spread of best practices. Standardization may also contribute to greater fairness, equity, reliability and validity in high stakes processes, and can provide stakeholders, including the public, with tangible reassurance and a sense of the stable and timeless. At the same time, contextual variation in medical education can afford myriad learning opportunities, and it can improve alignment between training and local workforce needs. The inevitable diversity of contexts for learning and practice renders any absolute standardization of programs, experiences, or outcomes an impossibility. CONCLUSIONS: The authors propose a number of ways to examine the interplay of contextual diversity and standardization and suggest three ways to move beyond an either/or stance. In reconciling the laudable goals of standardization and the realities of the innumerable contexts in which we train and deliver care, we are better positioned to design and deliver a medical education system that is globally responsible and locally engaged.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".