Development of the CT Scan Attitude Scale and Evaluation of Its Reliability and Validity
Bibliographic record
Abstract
PURPOSE: Ensuring that patients undergo examinations with confidence and ease is crucial. This study aims to develop a reliable and valid CT Scan Attitude Scale (CT-SAS) to measure attitudes toward CT scans objectively. METHODS: In Study 1, question items were developed based on preliminary surveys and prior research. A survey involving 497 screening participants was conducted to refine the scale. Factor analysis was employed to select appropriate items, estimate a factor model, and assess the reliability of the scale. In Study 2, the CT-SAS was administered to 496 university students, and its validity was evaluated by comparing their responses with those of screening participants. RESULTS: Study 1 resulted in the development of a 10-item, 3-factor scale, with all model fit indices meeting established criteria. Reliability coefficients (Cronbach's α) for each factor ranged from 0.850 to 0.751, indicating high internal consistency. In Study 2, university students demonstrated significantly higher scores on each factor, supporting the scale's validity. CONCLUSION: This study successfully developed and validated the CT Scan Attitude Scale, a tool for assessing attitudes toward CT scans. Future research should explore how interventions targeting examination attitudes can influence outcomes using this scale.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".