56. Proficiency at the end of practice predicts retention of a technical clinical skills
Bibliographic record
Abstract
A debate is emerging regarding the efficacy of proficiency based versus duration based training of technical skills. It is not clear whether the performance level attained at the end of practice (i.e., proficiency criteria), or the overall amount of practice performed during learning will best predict the retention of a technical clinical skill. 
 The skill learned was the single-handed double square-knot. Forty two trainees learned the skill through video-based instruction and were divided into three groups (14 participants per group) each with a specific criterion time to tie the knot (10, 15, and 20 seconds). Practice continued until participants completed the knot within their criterion time. The total number of trials, and the overall practice time required to obtain each respective criterion were recorded during practice. Participants returned one-week later for a timed retention test consisting of one trial of the knot tying skill with no video instruction.
 A multiple regression analysis tested whether the amount of practice, the total practice time, or the criterion reached at the end of practice was the best predictor of the time taken to perform the skill during retention. This analysis showed that the number of practice trials was highly correlated with total practice time (r = .82, p = .01), therefore total practice time was withdrawn as a predictor variable from the subsequent analysis. The regression showed that the only significant predictor of retention performance was the criterion reached at the end of practice (p = .03). The number of practice trials was not found to significantly predict the retention performance (p = .87).
 The results support the notion that proficiency based training results in better retention of a technical clinical skill in comparison to duration based approaches. This provides evidence for the introduction of proficiency based educational approaches in technical skills curricula.
 Jowett N, LeBlanc V, Xeroulis G, MacRae H, Dubrowski A. Surgical skill acquisition with self-directed practice using computer-based video training. Am J Surg. 2007; 193(2):237-42. 
 Gallagher AG, Ritter EM, Champion H, Higgins G, Fried MP, Moses G, Smith CD, Satava RM. Virtual reality simulation for the operating room: proficiency-based training as a paradigm shift in surgical skills training. Ann Surg. 2005; 241(2):364-72. 
 Van Sickle KR, Ritter EM, McClusky DA, Lederman A, Baghai M, Gallagher AG, Smith CD. Attempted establishment of proficiency levels for laparoscopic performance on a national scale using simulation: the results from the 2004 SAGES Minimally Invasive Surgical Trainer-Virtual Reality (MIST-VR) learning center study. Surg Endosc. 2007; 21(1):5-10.
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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 | 0.035 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.025 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".