How effective is self‐guided learning of clinical technical skills? It’s all about process
Bibliographic record
Abstract
OBJECTIVES: Mounting evidence suggests that trainees acquire psychomotor skills better when they are allowed self-guided access to instructional material and when they set goals that are related to performance processes rather than performance outcomes. The present study assessed whether self-guided access to instruction and the setting of process goals lead to better acquisition of clinical technical skills. METHODS: To learn wound closure skills, 48 medical students were randomly assigned to one of four groups in a 2 x 2 study design. Self-guided participants were able to access the instructional video freely, whereas control participants were restricted to watching only those video segments accessed by their matched self-guided participant. Each group was further divided into two subgroups, comprising a process goal subgroup, where participants set goals focused on performance mechanisms, and an outcome goal subgroup, where participants set goals focused on performance products. Performance on pre-, post-, retention and transfer tests was assessed with hand motion measures and expert evaluations. Group differences were evaluated using one-way anovas. RESULTS: The self-guided group with process goals showed greater skill retention than its matched control group, whereas the self-guided group with outcome goals did not. Furthermore, the groups with process goals performed better on the transfer test than the outcome goal groups. Outcome goal participants accessed the instructional video most frequently. CONCLUSIONS: Our findings advance the study of independent learning in medical education. Trainees used interactive and structured instructional materials to effectively self-guide their learning of clinical technical skills. However, a self-guided benefit was demonstrated only when trainees set process goals.
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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 | 0.006 | 0.019 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".