Differences in practice behaviours between expert musicians and non-musicians when learning a basic surgical skill
Bibliographic record
Abstract
Background: Researchers have hypothesized that years of daily practice on a musical instrument may lead to increased efficiency in practice behaviours during the learning of other fine motor skills [1]. Practice strategies in music have also been considered a suitable model for surgical training [2] and some believe that surgical outcomes might be improved by adopting musicians’ practice strategies [3].Objective: This study examines the practice behaviours of expert musicians attempting to learn a basic surgical skill, as a way to detect possible transfer of practice habits across domains. This paper investigates whether musicians differ from a control group in their selection and application of practice strategies, whether there are relationships between the choice of practice behaviours and performance scores, whether musicians progress more rapidly through the different phases of learning and, whether relationships exist between those who reach automaticity sooner and their choice of practice behaviours.Methods: Participants’ practice sessions during a knot-tying task (taught via instructional video) were video-recorded and treated according to the method of thematic analysis [4]. Coding was performed by two evaluators through an iterative process and statistical and descriptive analyses were conducted on practice behaviours. Information was also collected on instructional video navigation so that the use of replay, pause, rewind or fast-forward could be investigated.Results: Musicians and the control group participants favoured different practice behaviours; this was demonstrated in their choice of strategies, the importance they gave to each strategy, and the way they used the strategies over the two practice sessions. There was evidence that the group of expert musicians applied better practice strategies as the choice of practice behaviours correlated positively with performance scores and their capacity to reach automaticity.Conclusion: Our results suggest that music experts may be applying practice strategies developed during their music studies when learning a novel surgical skill. This raised the possibility that practice skills developed through years of dedicated work at their musical instrument, might be transferable when learning a motor skill in a different domain.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".