Evaluating the Influence of Goal Setting on Intravenous Catheterization Skill Acquisition and Transfer in a Hybrid Simulation Training Context
Bibliographic record
Abstract
INTRODUCTION: Educators often simplify complex tasks by setting learning objectives that focus trainees on isolated skills rather than the holistic task. We designed 2 sets of learning objectives for intravenous catheterization using goal setting theory. We hypothesized that setting holistic goals related to technical, cognitive, and communication skills would result in superior holistic performance, whereas setting isolated goals related to technical skills would result in superior technical performance. METHODS: We randomly assigned practicing health care professionals to set holistic (n = 14) or isolated (n = 15) goals. All watched an instructional video and studied a list of 9 goals specific to their group. Participants practiced independently in a hybrid simulation (standardized patient combined with an arm simulator). The first and the last practice trials were videotaped for analysis. One-week later, participants completed a transfer test in another hybrid simulation scenario. Blinded experts evaluated performance on all 3 trials using the Direct Observation of Procedural Skills tool. RESULTS: The holistic group scored higher than the isolated group on the holistic Direct Observation of Procedural Skills score for all 3 trials [mean (SD), 45.0 (9.16) vs. 38.4 (9.17); P = 0.01]. The isolated group did not perform better than the holistic group on the technical skills score [10.3 (2.73) vs. 11.6 (3.01); P = 0.11]. CONCLUSIONS: Our results suggest that asking learners to set holistic goals did not interfere with their attaining competent holistic and technical skills during hybrid simulation training. This exploratory trial provides preliminary evidence for how to consider integrating hybrid simulation into medical curricula and for the design of learning goals in simulation-based education.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".