Impact of COVID-19 on Confidence and Anxiety in Medical Students Related to Procedural Skills
Bibliographic record
Abstract
• Students who underwent preclerkship training during the pandemic had higher anxiety and lower confidence than those who had traditional training. • A surgical and procedural skill bootcamp increased confidence and decreased anxiety across both cohorts. • A 5 day in-person surgical and procedural skill bootcamp negated almost all significant differences between those who went through preclerkship during covid and those who did it prior to. During the COVID-19 pandemic, opportunities for hands-on surgical and procedural skills training and practice were significantly reduced, as many curricular activities were deferred or converted to a virtual format. This study focused on whether these changes contributed to increased anxiety and decreased confidence for medical students performing these skills. The Surgical Skills Technology Elective Program (SSTEP) is an annual five-day intensive procedural skills program after second-year medical school. Surveys assessing anxiety and confidence with respect to procedural skills were distributed and completed before and after SSTEP in 2016 and 2022. Pre-SSTEP scores were higher for anxiety and lower for confidence in the 2022 cohort compared to the prepandemic group. Post-SSTEP scores for anxiety and confidence were comparable between cohorts. Curricular changes and restrictions during the pandemic likely played a major role in the 2022 cohort having more anxiety and less confidence in their skills than prepandemic cohorts. However, these changes were effectively mitigated after participation in SSTEP. Medical schools should consider using and expanding on in-person bootcamps to support those with decreased exposure to surgical and procedural skills related to resource constraints and/or curricular changes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".