Designing an innovative professional development experience to build infection control professionals’ educational expertise
Bibliographic record
Abstract
Background: Advances in networked learning technologies have impacted our understanding and organization of teaching and learning. In the modern context of a learning society, conventional classroom-style education and transfer of knowledge is being challenged. Infection prevention and control (IPAC) educational practice must respond to the change that technology brings to teaching and learning. While education is an important component of IPAC professional practice, few Infection Control Professionals (ICPs) have formal pedagogical training. ICPs need support in shifting from teaching-as-telling approaches to becoming designers of contemporary active and engaged teaching and learning environments. Methods: To build ICP pedagogical expertise and practice within the Alberta Health Services (AHS) IPAC program, a Design-Based Research methodology was used to systematically engineer an intentionally disruptive professional development experience (PDE) for ICPs that aligned with contemporary teaching and learning strategies. The PDE was situated in the context of a Community of Learning (CoL) located within the ICPs’ workplace practice. Learning in the CoL was mediated through participation in collaborative design, teaching, and learning activities over a period of one year. Results: The PDE framework that emerged in this study facilitated changes in the AHS ICP CoL participants’ understanding of teaching and learning, their sense of identity as educators, and their educational practices. The core of the framework focused on designing for a flexible, responsive collaborative learning environment supported by four strategies: a) creating an awareness of ICP educational practice, b) building pedagogical knowledge, c) experiencing different teaching and learning strategies, and d) building ICPs’ identity as educators. Discussion: Creating conceptual change and new designs for teaching practice is not easy, as it involves significant transformation that can be uncomfortable and complex and often requires new ways of learning. This paper discusses the guiding principles used in the design of this intentionally disruptive yet positive and responsive learning experience to build the participating ICPs’ pedagogical expertise and practice.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".