Just-in-Time Continuing Education: Perceived and Unperceived, Pull and Push Taxonomy
Bibliographic record
Abstract
ABSTRACT: We live in a world where "just-in-time" (JiT) methodologies are increasingly used. Continuing professional development (CPD), including faculty development, has the opportunity to leverage online technologies in a JiT format to further support learner engagement and program sustainability. In this article, the authors propose a model that can serve as a taxonomy for defining and implementing JiT continuing education (JiTCE). The anatomy of JiTCE describes four mechanisms to address CPD needs and delivery procedures: perceived and unperceived, as well as pull and push (PUPP) taxonomy. JiTCE PUPP taxonomy defines four components for designing and developing a program with JiT: on-demand learning, subscription-based learning, performance feedback-driven learning, and data-driven learning. These methods, as backbones, use various online technologies, which offer fundamental support for JiTCE. Delivery systems and technologies are provided as specific examples for JiTCE throughout the article. JiTCE introduces a novel taxonomy to meet continuing education needs and provides an organized approach to design and deploy programming in a sustainable way. Online technologies are evolving everyday and are an indispensable part of both clinical practice and medical education. Pull-push and perceived-unperceived axes can help guide new opportunities for instructional designers and curriculum developers to leverage best practices to align with CPD offerings, which include cutting-edge technologies.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".