Validation of the Japanese version of the full and short form Trust in Oncologist Scale
Bibliographic record
Abstract
Abstract Objectives This study aimed to validate the Japanese versions of the Trust in Oncologist Scale (TiOS-J) and the TiOS-Short Form (TiOS-SF-J). Methods A cross-sectional web-based survey was conducted among cancer patients in Japan. The forward-backward translation method was used to develop the TiOS-J. The web-based survey was mailed to 633 people, of whom 309 responded. After 2 weeks, 103 among the 156 first-time respondents completed the second survey to verify the reliability of the retest method. The validity was evaluated by exploratory factor analysis (EFA), confirmatory factor analysis (CFA), Spearman’s correlation coefficients between the Patient Satisfaction Questionnaire-Japanese, willingness to recommend the oncologist, trust in health care, and number of oncological consultations. To evaluate reliability, Cronbach’s α and test–retest correlation were calculated. Results The theoretically driven four-factor model and the EFA-driven one-factor model of the full-form TiOS-J (18 items) did not result in an acceptable fit; however, CFA supported the one-dimensionality of the 5 items from the TiOS-SF-J ( χ 2 (5) = 12.36, p = 0.03, goodness-of-fit index = 0.984, adjusted goodness-of-fit index = 0.952, comparative fit index = 0.991, and root mean square error of approximation = 0.069). With regard to the reliability of TiOS-J and TiOS-SF-J, the Cronbach’s alpha values were 0.94 and 0.89, respectively; the test–retest values were 0.82 and 0.78. Significance of Results This study indicated that the TiOS-J and TiOS-SF-J are valid and reliable instruments for measuring patients’ trust in their oncologists and can be used to assess trust in oncologists for both clinical and research purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".