Self-directed practice schedule enhances learning of suturing skills
Bibliographic record
Abstract
BACKGROUND: Most preoperative surgical training programs experience challenges with the availability of expert surgeons to teach trainees. Some research suggests that trainees may benefit from being allowed to actively shape their learning environments, which could alleviate some of the time and resource pressures in surgical training. The purpose of this study was to investigate the effects of self-directed or prescribed practice schedules (random or blocked) on learning suturing skills. METHODS: Participants watched an instructional video for simple interrupted, vertical mattress and horizontal mattress suturing then completed a pretest to assess baseline skills. Participants were assigned to 1 of 4 practice groups: self-directed practice schedule, prescribed blocked practice schedule, prescribed random practice schedule or matched to the self-directed group (control). Practice of the skill was followed by a delayed (1 h) posttest. Improvement from pretest to posttest was determined based on differences in performance time and expert-based assessments. RESULTS: Analyses revealed a significant effect of group for difference in performance time of the simple interrupted suture. Random practice did not show the expected advantage for skill learning, but there was an advantage of self-directed practice. CONCLUSION: Self-directed practice schedules may be desirable for optimal learning of simple technical skills, even when expert instruction is available. Instructors must also take into account the interaction between task difficulty and conditions of practice to develop ideal training environments.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".