The Learning Loop: Conceptualizing Just‐in‐Time Faculty Development
Bibliographic record
Abstract
BACKGROUND: As technology advances, the gap between learning and doing continues to close-especially for frontline academic faculty and clinician educators. For busy clinician faculty members, it can be difficult to find time to engage in skills and professional development. Competing interests between clinical care and various forms of academic work (e.g., research, administration, education) all create challenges for traditional group-based and/or didactic faculty development. METHODS: The authors engaged in a synthetic narrative review of literature from several unrelated fields: learning technologies, medical education/health professions education, general/higher education. The aim for this review was to synthesize this pre-existing literature to propose a new conceptual model. RESULTS: , to guide the development of online faculty development for just-in-time delivery. CONCLUSIONS: is a new conceptual framework that may be of use to those engaging in online, digital learning design. Faculty developers, especially in emergency medicine, can integrate leading concepts from the technology-enhanced learning field (e.g., microlearning, micro-credentialing, badging) to create new types of learning experiences for their end-users.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".