Big Assumptions in Online and Blended Continuing Professional Development: Finding Our Way Forward Together
Bibliographic record
Abstract
ABSTRACT: Continuing professional development (CPD) providers and faculty face a practice gap between our knowledge of effective practices in CPD and our implementation of them, particularly in online environments. Developmental psychologists Bob Kegan and Lisa Lahey have attributed such knowledge-implementation gaps to an "Immunity to Change" rooted in tacit "Big Assumptions." These Big Assumptions produce fears or worries, reveal competing commitments, and result in actions or inactions that hinder intended change. We sought to understand the barriers to change in online and blended CPD, to support CPD leaders in pursuing their goals for optimal use of technology in CPD. This inquiry arose from the 13th National Continuing Professional Development Accreditation Conference of the Royal College of Physicians and Surgeons of Canada and the College of Family Physicians of Canada, a virtual conference held in October 2022. After introducing the Immunity to Change framework and best practices in online and blended learning, we invited audience members to list Big Assumptions in CPD through chat and polling software. These responses were analyzed and grouped into five interrelated Big Assumptions that suggest a number of key barriers to optimal implementation of online CPD. We present data that counter each Big Assumption along with practical approaches to facilitate desired change for CPD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.105 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.006 | 0.012 |
| 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".