The path to simulated learning: developing a valid and reliable tool to evaluate performance of radiological technology students in patient interactions.
Bibliographic record
Abstract
BACKGROUND: Simulation-enhanced education is being used in many health care curricula as an answer to an increasing demand for allied health professionals and a paucity of clinical sites crucial to providing requisite clinical education. The effective use of simulation may reduce and provide better use of the time required in clinical environments. If the use of simulation is to become a valuable addition to traditional clinical experiential learning, a reliable and valid measurement tool designed to measure a radiological technologist's performance in a practice environment is vital. OBJECTIVE: This study assessed the reliability of a clinical competency evaluation tool used in the radiological technology setting. The objective was to determine the inter-rater reliability of the evaluation tool as applied to six clinical scenarios. METHODS: In order to test the clinical competency evaluation tool, standardized patients portrayed radiological technologists and patients in six videotaped scenarios depicting one-on-one interactions between these two groups. Nine trained and qualified participants watched the videos and used the study tool to evaluate each radiological technologist's clinical communication competency. RESULTS: Inter-rater and intraclass correlations were generated to examine the internal consistency of rater scores on the videotaped scenarios. Face and content validity were established through an authenticity exercise involving content educator experts. CONCLUSION: Overall, this tool was shown to be a reliable, valid, feasible, and usable methodology to assess communication skills with clinical students. Further investigation of the tool is required to examine the tool's ability to reliably identify borderline or mixed skill level students under cultural and non-culture-based scenarios.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".