Practical Framework for Fostering a Positive Learning Environment
Bibliographic record
Abstract
The Council on Medical Student Education in Pediatrics continues the series about skills of, and strategies used by great clinical teachers. Great clinical teachers recognize that many things influence students’ learning, including what they teach, how they teach, and where they teach.1,2 “Where” refers to the learning environment. Although the term “environment” may conjure visions of trees, snow-capped mountains, and rain, the “learning environment” refers to the setting in which the curriculum exists. The learning environment includes the physical, social, and psychological context in which students learn and the overall atmosphere or culture pervading the setting where students and clinical teachers work and learn together.3,4 Although the learning environment of an institution is greatly influenced at the organizational level, this article will focus on elements under the control of individual teachers. We describe practical ways busy clinical teachers can foster a positive learning environment. A positive learning environment helps students succeed,5 affects their moral development,6 and models a humanistic approach to medicine.7 Recently, the Association of American Medical Colleges released a statement on the optimal learning environment.8 However, for decades, great clinical teachers have known that the learning environment affects ethical and personal development.9 Orienting students leads to improved preparedness for clinical work,10 and encouraging students to be active participants in the learning process leads to enhanced learning.11 In addition, the presence of exemplary role models enhances learning.12 Clinical teachers foster a positive learning environment in many practical ways (Table 1). In pediatrics, students worry about “breaking” a … Address correspondence to Susan L. Bannister, Department of Pediatrics, Faculty of Medicine, University of Calgary, 2888 Shaganappi Trail NW, Calgary, Alberta, Canada T3B 6A8. E-mail: susanl.bannister{at}albertahealthservices.ca
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".