Bibliographic record
Abstract
PROBLEM ADDRESSED: programs from June 2016 to May 2018 to determine users' awareness of the changes made to each program and to determine user engagement and satisfaction. OBJECTIVE OF PROGRAM: To assess changes in CPD program quality, the effects of changes on member understanding of credit reporting and engagement, data accuracy of credit reporting, and the perceptions of the CPD program certification process among providers. PROGRAM DESCRIPTION: Surveys and interviews were conducted with stakeholders from both groups, and consultations occurred with a third-party consultant. Administrative data and program files were also analyzed. More than 33 000 users (about 95% of all Mainpro+ participants) have accessed Mainpro+ since its launch. Satisfaction varies, with 31% of members and 39% of non-member Mainpro+ participants expressing difficulty entering activities. Most users (79%) understand the changes implemented. Among CERT+ users, half (50%) find the platform easy to use, whereas 23% find it difficult; 86% find the CPD program submission requirements somewhat or very clear. Project limitations include difficulty comparing data between phases and a lack of qualitative data. CONCLUSION: The College of Family Physicians of Canada anticipates these program enhancements will lead to higher-quality CPD programs and greater clarity and efficiency for members and CPD providers. All collected data will be used to inform ongoing improvements to both platforms to improve the experience of all users.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.205 | 0.030 |
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".