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Record W4414106902 · doi:10.6009/jjrt.25-1575

Development of the CT Scan Attitude Scale and Evaluation of Its Reliability and Validity

2025· article· en· W4414106902 on OpenAlexaff
Tomonari Tomitaka, Kohsuke Yamamoto, Rikuta Ishigaki, Kentaro Inomata, Tian‐Li Bo, Natsumi Kuwabara

Bibliographic record

VenueJapanese Journal of Radiological Technology · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsWorkplace Health, Safety and Compensation Commission
FundersJapan Society for the Promotion of Science
KeywordsReliability (semiconductor)Scale (ratio)Computed tomographyPsychological intervention

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.115
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.115
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.374
GPT teacher head0.468
Teacher spread0.094 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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