Colonoscopy Trainers Experience Greater Stress During Insertion than Withdrawal: Implications for Endoscopic Curricula
Bibliographic record
Abstract
Abstract Background Optimal colonoscopy training curricula should minimize stress and cognitive load. This study aimed to determine whether withdrawal or insertion colonoscopy skills training is associated with less stress or cognitive load for trainees or trainers. Methods In Phase I, participants were randomized to train on either insertion or withdrawal in a simulated environment. In Phase II, participants were randomized to begin with either insertion or withdrawal in patient encounters. Salivary cortisol levels, heart rate, and State-Trait Anxiety Inventory (STAI) surveys were used to assess stress in trainees and trainers. NASA Task Load Index (TLX) survey was used to assess cognitive workload in trainees. Results In Phase I, trainee stress increased during the simulation training during both withdrawal and insertion compared to baseline, while trainer stress changed minimally. Cognitive load was higher for trainees during withdrawal (P = 0.005). In Phase II, trainers’ STAI scores were greater during insertion training (P = 0.013). Trainees’ stress was highest prior to beginning patient training and decreased during training, while trainer’s stress increased during training. Trainees reported insertion training being of greater value (70.0%), while trainers reported withdrawal was preferred (77.8%). Conclusion Trainees and trainers exhibit important differences in stress during colonoscopy skills training. Trainees reported more stress during simulation training and greatest cognitive load during simulation withdrawal, whereas trainers reported greatest stress during patient encounters, particularly training of insertion techniques. Attention to the effect of stress on trainees and trainers and the drivers of stress is warranted and could be incorporated in competency based medical education.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".