102 Self-Directed Learning Versus a Structured Cme Course to Assess Physicians' Knowledge of Sedation
Bibliographic record
Abstract
Pediatric sedation guidelines were developed for a tertiary care Pediatric Emergency Department and a continuing medical education course in Pediatric Sedation was organized to facilitate physicians' education. A needs assessment survey and focus group discussions were conducted to ensure compatibility with educational needs of the target audience. To evaluate the effectiveness of a sedation course to improve physician knowledge on pediatric procedural sedation practices and guidelines versus individual learning. Staff Physicians and fellows were invited to attend a 4-hour multi-faceted sedation course. All physicians received the course material for individual study two weeks prior to the course. All consenting physicians were randomly assigned to one of two groups. Group 1 (n=24) was assigned the multiple-choice exam without receiving any formal teaching (pre-test). Group 2 (n=23) wrote the exam after attending the course (post-test). The course consisted of didactic teaching and small group cased-based discussions. The multiple choice questions were piloted for construct and content validity. Means and standard deviation of multiple choice exam scores for both groups were calculated. Student's t test was used to compare scores of the multiple-choice examination. The group mean of pre-test scores, 71.1% (SD 11.8, range 50–86.6%), was significantly different (p<0.0001) from the group mean of the post-test scores, 85.1% (SD 6.0, range of 75.8–96.5%). A multi-faceted sedation course is more effective in improving physician knowledge and understanding of sedation guidelines than unstructured self-education as measured by a multiple-choice exam.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".