CME for child psychiatrists: recommendations for learners, planners and presenters.
Bibliographic record
Abstract
INTRODUCTION: Medical school and residency are only the beginning of a child psychiatrist's education. For the rest of her/his career, a child psychiatrist will need to learn on an ongoing basis. There will always be new understandings, new treatments, new issues to master. Child psychiatrists will always need to further their knowledge, develop new skills, and improve existing skills. For these reasons at very least, all child psychiatrists will need to participate in Continuing Medical Education (CME) activities. Many child psychiatrists will also be involved in the design and delivery of these CME activities. In both cases, understanding more about the effectiveness of CME will be important to the decisions they make. METHOD: This article itself is not a systematic review of the literature, but it will highlight some of the important findings from existing systematic reviews of the CME literature. Based on these findings, the article will make recommendations for both child psychiatrists as learners and child psychiatrists as CME presenters. RESULTS: As learners, child psychiatrists need to be able to select CME activities that are most likely to lead to improvements in their practices. As planners and presenters, child psychiatrists need to design and deliver CME activities that are most likely to improve the practices of their target audiences. However, not all child psychiatrists have the time to review the CME literature in addition to reviewing the other bodies of literature relevant to their practices. CONCLUSION: Thus, the purpose of this article is to provide an overview of the key findings in the CME literature, focusing on the effectiveness of CME.
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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 | 0.025 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 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".