75. Learning on the run - Practical strategies for physician learning
Bibliographic record
Abstract
As a part of the Center for Learning in Practice’s (CLIP) mandate, a white paper series on topics of relevance to the educational needs of fellows was developed. The first in this series of white papers, was one entitled: Lifelong Learning White Paper - Supporting Physician Lifelong Learning: Strategies, Tools and Recommendations. This white paper focused on a variety of themes including the concept of ‘learning on the run’, which means that learning takes place wherever you are and occurs on a daily basis over the course of one’s work routine. In other words, learning and the learning context is driven by one’s practice context as well as by one’s own career goals and needs. The center for learning in practice is currently producing a series of thematic monographs/booklets for physicians based on the white papers, the first of which is entitled: Learning on the run- practical strategies for physician learning. The purpose of these monographs are to assist physicians with their learning and practice needs and contain a section on how tools and programs within the Maintenance of Certification (MOC) program can enhance and contribute to physician learning strategies. This poster details the content of a draft monograph on ‘learning on the run’ for physicians to use and comment on. These comments will be used to refine and enhance the monograph in order for CLIP to disseminate it more widely across North America. The monograph can also be accessed under CLIP’s section of The Royal College of Physicians and Surgeons of Canada website - http://rcpsc.medical.org/clip/index.php
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.044 |
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".