Rapid Knowledge Mobilization and Continuing Professional Development: Educational Responses to COVID-19
Bibliographic record
Abstract
INTRODUCTION: The field of Continuing Professional Development (CPD) has a role to play in supporting health care professionals as they respond to the COVID-19 pandemic. However, the evolving science of COVID-19, the need for quick action, and the disruption of conventional knowledge networks pose challenges to existing CPD practices. To meet these emergent and rapidly evolving needs, what is required is an approach to CPD that draws insights from the domain of knowledge mobilization (KMb). METHODS: This short report describes a research protocol for exploring rapid KMb responses to COVID-19 at one Canadian academic teaching hospital. The proposed research will proceed as a case study using a mixed methods design collecting quantitative (surveys and Web site use metrics) and qualitative data (interviews) from individuals involved in developing, using, and supporting the KMb resources. Analysis will proceed in two phases: descriptive analysis of data to share insights and integrative analysis of data to build theory. RESULTS: Results from this study will inform the immediate KMb and CPD contribution to the COVID-19 response. DISCUSSION: Findings from this study will also make a broader contribution to the field of CPD, theoretically informing intersections between KMb and CPD and therefore contributing to an integrated science of CPD.
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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 | 0.025 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".