Gathering Dust—Resistance to Simulator-based Deliberate Practice in Microsurgical Training
Bibliographic record
Abstract
Abstract Background Despite unrestricted access to a simulated microsurgery model, learners have not consistently self-regulated their learning by completing practice. This paper explores the lived experience of learners regarding how practice is perceived and why it is resisted. Methods A qualitative study was conducted, including recorded and transcribed focus groups and semistructured interviews. First and second pass coding was conducted by one reviewer, with feedback from another. Transcripts were analyzed with a constant comparative approach customary to thematic analysis. Theory was engaged to help explain and support the findings. The study was undertaken at the University of Calgary plastic surgery residency training program in Calgary, Alberta, Canada, involving 15 informants (9 residents and 6 surgeons). Results Four themes emerged: (1) barriers to practice, (2) motivation to practice, (3) owning learning/solutioning, and (4) expectations of practice. Competing priorities and time constraints were barriers. Motivation to practice ranged from extrinsic (gaining access to the next course) to intrinsic (providing optimal patient care). Learners described a range of ownership of learning and depth of effort at solutioning of practice opportunities. Learners expressed high expectations around model fidelity, ease of setup, and feedback. Learners self-regulating their learning, with surgeons acculturating practice at work, can overcome some barriers. As per self-determination theory (SDT), learners need explicit linkage to how the task aligns with their goals. Assessment may be required to motivate learners. In respect of adult learning theory, homework needs to be allocated by a respected trainer. Modeling simulation practice may encourage adult learners. Finally, the tenets of deliberate practice (DP) need to be explained in order that learners can optimize their practice time. Conclusion Microsurgical simulation practice is valued but barriers exist that invite resolution. Assisting residents to overcome barriers, maintain motivation, take ownership, and assimilate DP will help improve their microsurgery practice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".