The clinical learning environment
Bibliographic record
Abstract
Learning in a clinical context is foundational in the training of health professionals; there is simply no alternative. The subject of the clinical learning environment (CLE) is at the forefront of discussions. In this introduction to a themed issue on the CLE, we present an expanded conceptual model that approaches the CLE through six different lenses, termed "avenues:" architectural, digital, diversity and inclusion, education, psychological, and sociocultural, with each avenue represented by a paper. The aim is to facilitate dialog around the contributions of different academic disciplines to research on the CLE. Collectively the papers highlight the overlap between the various "avenues" in how they influence each other, and how they collectively have shaped the work to understand and improve the CLE. The expectation is that the various avenues can add to existing knowledge and create new ideas for interventions to improve the clinical learning environment across nations for learners and teachers with the ultimate aim of improving patient care. Research and efforts to improve the CLE are critical to learning, professional socialization and well-being for trainees as they learn and participate in patient care, and to the quality of care they will deliver over decades of practice after graduation.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.015 |
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".