Development and psychometric testing of the nursing student satisfaction scale for the associate nursing programs
Bibliographic record
Abstract
Background: The purpose of this nationwide study was to assess psychometric properties of the Nursing Student Satisfaction Scale (NSSS) for measuring student satisfaction with nursing programs. Methods: This methodological study addressed the development, evaluation, and validation of a newly developed instrument using a cross-sectional design. Proportionally stratified random sampling was utilized to select 138 Associate in Science in Nursing (ASN) programs for participation. Results: Evidence of psychometric evaluation indicated that the internal consistency reliability was consistently acceptable throughout a previous 3-year psychometric evaluation study to this methodological study. The Curriculum and teaching, Professional social interaction, and Environment (CPE) 3-factor model of the NSSS was suggested by the exploratory factor analysis and was supported by the confirmatory factor analysis based on the results of “goodness-of-fit” test. Conclusions: The NSSS demonstrates sound psychometric properties and provides a theory-based approach to the measurement of nursing student satisfaction.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".