Are online learning modules an effective way to deliver hand trauma management continuing medical education to emergency physicians?
Bibliographic record
Abstract
BACKGROUND: The enormity of modern medical knowledge and the rapidity of change have created increased need for ongoing or continuing medical education (CME) for physicians. Online CME is attractive for its availability at any time and any place, low cost and potentially increased effectiveness compared with traditional face-to-face delivery. OBJECTIVE: To determine whether online CME modules are an effective method for delivering plastic surgery CME to primary care physicians. METHODS: A needs assessment survey was conducted among all emergency and family physicians in Nova Scotia. Results indicated that this type of program was appealing, and that hand trauma related topics were most desired for CME. 7 Lesson Builder (SoftChalk LLC, www.softchalk.com) was used to construct a multimedia e-learning module that was distributed along with a pretest, post-test and feedback questionnaire. Quantitative (pre- and post-test scores) and qualitative (feedback responses) data were analyzed. RESULTS: The 32 participants who completed the study indicated that it was a positive and enjoyable experience, and that there was a need for more resources like this. Compared with pretest scores, there was a significant gain in knowledge following completion of the module (P=0.001). CONCLUSION: The present study demonstrated that an e-learning format is attractive for this population and effective in increasing knowledge. This positive outcome will lead to development of additional modules.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".