An Online Educational Module to Improve General Internal Medicine Trainees’ Knowledge and Comfort in Managing Acute Complications of Sickle Cell Disease
Bibliographic record
Abstract
BACKGROUND: Sickle cell disease (SCD) is a genetic disorder characterized by hemolytic anemia and vaso-occlusive episodes. Many adults with SCD require hospitalization for the management of acute manifestations. There appears to be a gap in the level of knowledge that general internal medicine (GIM) trainees possess regarding the treatment and management of SCD. METHODS: We created an online module that helps medical residents improve their knowledge about SCD. Participants were exposed to different scenarios related to SCD. A pre-and posttests were administered to detect improvement in knowledge and comfort when managing SCD and its acute complications. RESULTS: Thirty consecutive GIM residents participated. Of these, 20 completed the demographic survey, 17 completed the pretest, and 10 completed the posttest and usability survey. The median time to complete the module was 68 min. Fifty percent of participants were 1 st -year postgraduation from medical school. Forty-five percent stated that they were uncomfortable managing patients with SCD 80% had cared for at least 1–5 patients with SCD. All reported having no SCD lectures in residency. The median pretest score was 12/20 (range, 8–15) and posttest score was 20.5/25 (range, 13–24). CONCLUSIONS: Residents agreed that the module was useful, helps understanding about SCD and its complication, and aids with clinical duties. This module demonstrated to be an effective educational tool that can support resident education during their internal medicine. Further strategies will be needed to improve the delivery of online modules to keep residents engaged with the module and attaint full benefit from these activities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".