Controlling Quality in CME/CPD by Measuring and Illuminating Bias
Bibliographic record
Abstract
INTRODUCTION: There has been a surge of interest in the area of bias in industry-supported continuing medical education/continuing professional development (CME/CPD) activities. In 2007, we published our first study on measuring bias in CME, demonstrating that our assessment tool was valid and reliable. In light of the increasing interest in this area, and building on our experience, we wanted to further understand the application of this tool in different environments. We invited other CME/CPD providers from multiple sites in Canada to participate in a second CME bias study. METHODS: A new steering committee was established with representatives from 5 academic CME/CPD offices nationally, the Royal College of Physicians and Surgeons, and the College of Family Physicians of Canada to outline the project in terms of review of the literature, refining items on the tool, updating the training guide for implementation, and establishing a resource Web site for reviewers. Training involved a train-the-trainer session with the event coordinators at each of the 5 participating centers via videoconferencing. RESULTS: The content reviews from the study showed moderate inter-rater reliability (ICC = 0.54), and the live reviews showed poor overall inter-rater reliability; however, one center achieved substantial inter-rater reliability (ICC = 0.68). DISCUSSION: The analysis from this study suggests that the tool can be used as a part of a multistage process to introduce quality control mechanisms to help raise standards for CME/CPD. It is imperative to develop a cost-effective standardized training protocol that can be implemented at all sites to maximize the reliability of the tool.
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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 | 0.561 | 0.725 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".