Evaluating Change in Skill Performance Over Time and Practice Context in Introductory Fieldwork Simulation
Bibliographic record
Abstract
Simulation has been recognized for its ability to develop competency-level skills and as a replacement for some introductory fieldwork (FW) hours. This study explored how occupational therapy competency-related skills developed over sequential in-person simulations across health practice contexts during Level 1 FW. Entry-to-practice occupational therapy students (N = 66) participated in six sequential, formative, Level 1 FW simulations. The first three sequential simulations (the same patient case evolves in each successive interaction) included a trained simulated patient in a community mental health context and the following three engaged a trained simulated inpatient in a physical health context. Evaluation rubric variables included selected Competencies for Occupational Therapists in Canada (2021) scaffolded to performance expectations at an introductory Level 1 FW placement level. Quantitative pre-post comparison design with secondary data analysis was analyzed using Wilcoxon signed-rank test and ordered logistic regression. Each additional simulation demonstrated significant increases in the odds of improved performance in clinical skills, clinical decision making, responding to evolving patient’s needs and priorities, identifying their own strengths and weaknesses, articulating clinical reasoning, and receiving constructive criticism. However, students’ skills in the physical health context for decision-making and responding to the patient’s needs and priorities did not demonstrate the same improvement trajectories as the mental health context. Sequential simulations are an effective modality for developing Level 1 competency related skills in different practice contexts. Depending on the competency-related practice skill and context, three or more formative unfolding simulations in that context may be needed for a significant improvement.
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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 | 0.004 | 0.025 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".