What the COVID-19 Pandemic Can Teach Health Professionals About Continuing Professional Development
Bibliographic record
Abstract
The world’s health care providers have realized that being agile in their thinking and growth in times of rapid change is paramount and that continuing education can be a key facet of the future of health care. As the world recovers from the COVID-19 pandemic, educators at academic health centers are faced with a crucial question: How can continuing professional development (CPD) within teams and health systems be improved so that health care providers will be ready for the next disruption? How can new information about the next disruption be collected and disseminated so that interprofessional teams will be able to effectively and efficiently manage a new disease, new information, or new procedures and keep themselves safe? Unlike undergraduate and graduate/postgraduate education, CPD does not always have an identified educational home and has had uneven and limited innovation during the pandemic. In this commentary, the authors explore the barriers to change in this sector and propose 4 principles that may serve to guide a way forward: identifying a home for interprofessional continuing education at academic health centers, improving workplace-based learning, enhancing assessment for individuals within health care teams, and creating a culture of continuous learning that promotes population health.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".