Changing Physician Performance: A Systematic Review and Meta-Analysis (75 years 1950-2024) of the Effect of Continuing Medical Education Strategies, Continuous Professional Development and knowledge Translation
Bibliographic record
Abstract
ABSTRACT Introduction Medical education has always been considered an essential component for improving the knowledg and performance of physicians and medical assistants. However, this education must be carried out continuously t update the knowledge of physicians and improve their performance. Various strategies are used to achieve this goal, the most important of which are CME, CPD, and CBME strategies. This study aimed to investigate the effect of using these strategies on improving the performance of physicians in the world using a systematic review method. Methods This study was conducted using a systematic review method on the effect of educational strategies o improving the performance of physicians in the world. Accordingly, all relevant articles published between 195 and 2024 were extracted and reviewed through a search in the scientific databases PubMed/Medline, Web of Science, ProQuest, and Scopus. The quality of the articles was assessed using the CONSORT checklist an Newcastle-Ottawa Scale. Results twenty articles conducted between 1993 and 2024 were included in the systematic review process. According to the findings, providing continuing education based on CME, CPD, CBME, and knowledge translatio strategies positively affected the knowledge, skills, and performance of physicians and medical assistants i providing health services. This can be used to train medical students and practicing physicians on an ongoing basis. Conclusion Medical education using CME, CPD, and CBME strategies can improve physicians’ knowledge and performance, and subsequently increase job satisfaction, self-efficacy, communication skills, medical knowledge, performance, and skill. However, individual, structural, and content limitations that can prevent success in learnin and training programs should be considered and addressed.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".