Observations on the 2016 World Congress on Continuing Professional Development: Advancing Learning and Care in the Health Professions
Bibliographic record
Abstract
The 2016 World Congress on Continuing Professional Development: Advancing Learning and Care in the Health Professions took place in San Diego, California, March 17-19, 2016. Hosts were the Association for Hospital Medical Education (AHME), Alliance for Continuing Education in the Health Professionals (ACEhp), and Society for Academic Continuing Medical Education (SACME). The target audience was the international community working to improve medical (CME), nursing (CNE), pharmacy (CPE), and interprofessional (CIPE) continuing education (CE) and continuing professional development (CPD). Goals included: addressing patients' concerns and needs; advancing global medical and interprofessional health sciences education; utilizing learning to address health disparities; and promoting international cooperation. The five keynote speakers were: patient advocate Alicia Cole ("Why What We Do Matters: The Patients Voice"); linguist Lorelei Lingard ("Myths about Healthcare Teamwork and Their Implications for How We Understand Competence"); futurist and philosopher Alex Jadad ("What Do We Need to Protect at All Costs in the 21st Century?"); ethicist and change agent Zeke Emanuel ("Learn to Change: Teaching Toward a Shifting Healthcare Horizon"); and technology innovator Stephen Downes ("From Individual to Community: The Learning Is in the Doing"). Organizers announced the new Dave Davis Distinguished Award for Excellence in Mentorship in Continuing Professional Development to honor the career of David Davis, MD, in CME/CPD scholarship in Canada, the United States, and beyond. Participants valued the emphasis on interprofessional education and practice, the importance of integrating the patient voice, the effectiveness of flipped classroom methods, and the power of collective competency theories. Attendee-respondents encouraged Congress planners to continue to strive for a broad global audience and themes of international interest.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".