Evaluation Of The Efficacy Of E-Learning In Increasing Theoretical Knowledge And Inducing Behavior Change Amongst Vascular Surgery Trainees
Bibliographic record
Abstract
Background: In June 2019, new vascular surgery societal guidelines were released for the management of patients with chronic limb-threatening ischemia (CLTI). These guidelines were judged to be essential for clinicians who treat patients with CLTI, and we believed they represented an important and timely teaching opportunity for the vascular surgery community. Therefore, we decided to create an online curriculum, tailored for the needs of vascular surgery trainees, based on evidence-based teaching strategies and meticulous curriculum design, to teach key concepts of CLTI management and to test its efficacy in improving theoretical knowledge and in inducing behavior change amongst vascular surgery trainees in Canada. Methods: Participants were recruited from the 10 vascular surgery residency programs across Canada. The study uses a quasi-experimental design. Participants were asked to complete a knowledge check before and after interacting with our module. We also used pre- and post-intervention surveys to evaluate learners’ satisfaction with the module and impact on behaviour change. Results: Pre- and post-intervention test scores were compared using paired t tests for the 21 study participants. On average, learners scored 33 percentage points higher (p.0005) after interacting with the module. This increase was significantly greater among senior residents when compared with junior residents (+31 vs +35 percentage points higher), whereas baseline test scores were similar between these two subgroups. Learners’ satisfaction with the module was extremely high and we found that all participants would recommend this module to their peers and would like to engage in similar activities in the future. When evaluating behavior change, we found a trend towards improvement of guidelines compliant prescription patterns after interaction with the module. Indeed, prescriptions of post-procedural antiplatelet therapy were more frequently made according to guidelines recommendations after interaction with the module, although this finding was not statistically significant. Conclusion: Our study showed that online learning is a powerful tool in surgical residency programs and that residents greatly appreciate this type of educational activity. More research looking at the effect of e-learning on inducing behavior change are needed to prove the efficacy of e-learning in that specific area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".