Psychometric Testing of the Student Evaluation of Clinical Educational Environment Inventory in Greek Nursing Students
Bibliographic record
Abstract
INTRODUCTION: The need for translation and validation of an assessment tool regarding the Clinical Learning Environment of nursing students in Greece is imperative, given that inappropriate research tools are frequently used. Τhe aim of this study was to validate and psychometrically test the Greek translation of the Student Evaluation of Clinical Educational Environment (SECEE) Version 3 Inventory, with a sample of senior nursing students during their clinical practice. METHODS: Following a formal “forward-backward” method to translate the original SECEE into Greek, the scale was administered to 130 senior students. They also completed the Clinical Learning Environment and Supervision (CLES) Scale. Validity and reliability analyses were performed. RESULTS: Cronbach’s alpha coefficient for the SECEE subscales score was 0.89 for Instructor Facilitation of Learning (IFL), 0.84 for Learning Opportunities (LO) and 0.84 for Preceptor Facilitation of Learning (PFL). Test-retest reliability analysis in a subgroup of students (n=40) revealed good short term stability over a two week interval. Confirmatory factor analysis confirmed the three factor subscales for the Greek translation, as in the original scale. Construct validity was supported through the scale’s moderate correlation with CLES subscales, ranged from 0.163 to 0.317 for IFL, from 0.387 to 0.445 for LO and from 0.443 to 0.537 for PFL. CONCLUSIONS: The Greek version of the SECEE is a psychometrically sound instrument that can be usefully implemented into clinical education to identify appropriate clinical sites and provide information about student perceptions regarding the adequacy of learning opportunities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".