Part of the Medical Education Commons Recommended Citation
Bibliographic record
Abstract
Credits have traditionally served as an incentive for physicians to participate in the continuing medical education (CME), which is the means for life-long learning. The public at large has granted special rights to the profession of medicine giving them their most precious commodity; their lives. The providers of CME have a responsibility to maintain and update physicians' knowledge and competency.1 United States and Canada have been using continuing medical education credit to measure physician's participation in education for more than 50 years. It has been proven that engaging in lifelong learning would lead to a change in physician’s practice and improved patient care and outcomes.2 In this perspective, we have specifically defined the criteria for awarding CME credits to encourage practicing physicians in Pakistan and particularly within our University setting; as no system has yet been developed to measure and certify physician's participation in educational events. However, this could be replicated to award credits to other continuing professional development (CPD) activities. American Medical Association Physician's Recognition Award (AMA PRA), was emerged in 1968, in which live educational activities i.e. Category I was designated by accredited providers, whereas the rest five categories included various types of self-directed activities. In 1985, the AMA (PRA) was defined into categories 1 and 2 that made physicians fixed to attain 150 credit hours every 3 years with a minimum of 60 credit hours participation in Category I, i.e. live CME activities. The AMA and the Accreditation Council for
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.862 | 0.808 |
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".