10. Designing an online curriculum supporting risk management and patient safety
Bibliographic record
Abstract
The Canadian Medical Protective Association (CMPA) is a not for profit mutual defence organization with a mandate to provide medico-legal assistance to physician members and to educate health professionals on managing risk and enhancing patient safety. To expand the outreach to its 72,000 member physicians, the CMPA built an online learning curriculum of risk management and patient safety materials in 2006. These activities are mapped to the real needs of members ensuring the activities are relevant. Eight major categories were developed containing both online courses and articles. Each course and article is mapped to the RCPSC's CanMEDS roles and the CFPC's Four Principles. This poster shares the CMPA’s experience in designing an online patient safety curriculum within the context of medico-legal risk management and provides an inventory of materials linked to the CanMEDS roles. Our formula for creation of an online curriculum included basing the educational content on real needs of member physicians; using case studies to teach concepts; and, monitoring and evaluating process and outcomes. The objectives are to explain the benefits of curricular approach for course planning across the continuum in medical education; outline the utility of the CanMEDS roles in organizing the risk management and patient safety medical education curriculum; describe the progress of CMPA's online learning system; and, outline the potential for moving the curriculum of online learning materials and resources into medical schools.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".