The novice objective structured clinical evaluation tool: psychometric testing
Bibliographic record
Abstract
AIM: This paper is a report of a study of the psychometric properties of the Novice Objective Structured Clinical Evaluation Tool. BACKGROUND: A collaborative undergraduate nursing programme is currently using an objective structured clinical evaluation at the conclusion of the first nursing clinical course to determine student competence as a component of quality and safety education. However, the reliability and validity of the assessment tool has not been established. METHODS: Psychometric testing was conducted with a convenience sample of 565 nursing students. Data were collected during three consecutive years from 2002 to 2004. Exploratory factor analysis and reliability testing were conducted on this 25-item tool. RESULTS: Principal axis factoring method identified two factors through the orthogonal, oblimin and promax rotations: Factor 1 Safety and Factor 2, Anticipation. Spearman-Brown's result for Factor 1 was 0·93 and for Factor 2 was 0·77. Cronbach's alpha was.94 for Factor 1 and 0·71 for Factor 2. CONCLUSION: The tool was found to have adequate construct validity and reliability. Its stability should be tested by conducting test-retest analysis. Equivalency dimensions of reliability should be evaluated by looking at interrater reliability. This tool shows merit for assessing elements of quality and safety 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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".