Clinical learning environments: Updates
Bibliographic record
Abstract
Academic exploration of the Clinical Learning Environment (CLE) has evolved over time. In 2009, Asch et al. drew a direct connection between the CLE, training outcomes, and patient care, highlighting the importance of the CLE and inspiring further research in this domain. With a growing body of evidence articulating its significance, several organizations were prompted to publish conceptualizations of the CLE as a discrete concept, such that its components may be measured and thus improved upon. Since then, it has become increasingly clear that the CLE is not an isolated entity; it is continuously molded by external pressures including the intensifying polarizations within our global society and the political ramifications of these divides. And yet, the nuances of how these external forces influence our CLE, including how we should, or should not, adapt to accommodate them, has not yet been explicitly captured. In this commentary we will summarize the academic history of CLE, review how the shifting global landscape has influenced the CLE, and propose a way forward. As a professional community, we must understand the impact of these external factors such that we may proactively adapt and ensure quality training outcomes and positive outcomes for our patients.
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 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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
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".