Redesigning continuing professional development: Harnessing design thinking to go from needs assessment to mandate
Bibliographic record
Abstract
BACKGROUND: The world of medicine is constantly changing, and with it the continuing professional development (CPD) needs of physicians. As the CPD landscape is shifting away from unidirectional delivery of knowledge through live large group learning (conferences) and is placing increased emphasis on new approaches for skills training not taught a decade ago, a new approach is needed. APPROACH: Using design thinking techniques, we hosted a full-day retreat for emergency medicine stakeholders in Hamilton and the surrounding region. Prior to the retreat we collected medico-legal data on emergency physicians in our region and performed a needs assessment survey. At the retreat, we had participants brainstorm ideas for CPD, generate archetypes for end-users, then generate solutions to the problems they had identified. These proposals were presented to the larger group for feedback and refinement. EVALUATION: The Design Thinking Retreat generated five main pillars for action by our CPD team. 1) Simulation/procedural learning (staff simulation, procedural skills day, in situ simulation); 2) Asynchronous learning (website and podcast); 3) Synchronous learning (small group sessions for staff); 4) Community connectivity (online platform for collaboration and communication); and 5) Coaching & mentorship (focused coaching for specific practice improvement, improved onboarding for new staff). REFLECTION: These ideas have vastly increased engagement in CPD. Stakeholder consultation via design thinking may be a key approach for educators to use.
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 | 0.098 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".