Pivoting Continuing Professional Development During the COVID-19 Pandemic: A Narrative Scoping Review of Adaptations and Innovations
Bibliographic record
Abstract
INTRODUCTION: Most formal continuing professional development (CPD) opportunities were offered in person until March 2020 when the COVID-19 pandemic disrupted traditional structures of CPD offerings. The authors explored the adaptations and innovations in CPD that were strengthened or newly created during the first 16 months of the pandemic. METHODS: The objectives of the narrative review were to answer the following questions: (1) what types of adaptations to CPD innovations are described? and (2) what may shape future innovations in CPD? The following databases were searched: Medline, Embase, CINAHL, and ERIC to identify the literature published between March 2020 to July 2021. The authors conducted a comprehensive search by including all study types that described adaptations and/or innovations in CPD during the stated pandemic period. RESULTS: Of the 8295 citations retrieved from databases, 191 satisfied the inclusion criteria. The authors found three categories to describe adaptations to CPD innovations: (1) creation of new online resources, (2) increased use of the existing online platforms/software to deliver CPD, and (3) use of simulation for teaching and learning. Reported advantages and disadvantages associated with these adaptations included logistical, interactional, and capacity building elements. The review identified five potential future CPD innovations: (1) empirical research on the effectiveness of virtual learning; (2) novel roles and ways of thinking; (3) learning from other disciplines beyond medicine; (4) formation of a global perspective; and (5) emerging wellness initiatives. DISCUSSION: This review provided an overview of the adaptations and innovations that may shape the future of CPD beyond the pandemic.
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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 | 0.026 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".