Surgical residents’ approach to training: are elements of deliberate practice observed?
Bibliographic record
Abstract
Background: Deliberate practice research has consistently shown that intense, concentrated, goal-oriented practice in a focused domain, such as medicine, can improve skill development and performance. To date, little is known about how surgical residents approach their surgical training, how they evaluate their current weaknesses, and how they plan to transition from one milestone to another. Without knowledge of residents’ role in their development, educators miss the opportunity to optimize progression of these lifelong learning skills. Therefore, the purpose of this study was to gain a better understanding of how surgical residents approach their surgical training from the perspective of the surgical residents themselves and to explore if elements of deliberate practice are observed. Methods: Eight surgical trainees participated in one of two focus groups depending on their training level (five junior residents; three senior residents). With the exploratory nature of this research, a focus group methodology was utilized. Results: By employing both deductive and inductive thematic analysis techniques, three themes were extracted from the data: learning resources and strategies, role of a junior/senior, and approaching weaknesses. Conclusions: Although elements of deliberate practice were discussed, higher functioning is necessary to achieve performance excellence, leading to improved patient outcomes.
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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".