Trust under the microscope: psychometric evaluation of the Persian version of the Trust in Multidimensional Healthcare Systems Scale
Bibliographic record
Abstract
BACKGROUND: Trust in healthcare systems is an essential determinant of patient satisfaction and healthcare outcomes. Understanding and measuring this trust is essential for improving healthcare services. Due to the lack of a valid and reliable tool in Iran for measuring trust in multidimensional healthcare systems, this study aimed to evaluate the psychometric properties of the Persian version of the Trust in Multidimensional Healthcare Systems Scale (P-TIMHSS). METHODS: This cross-sectional study was conducted in 2024. The questionnaire was distributed online to family members of students at Iran University of Medical Sciences (N = 411). Face and content validity were assessed qualitatively. Participants were randomly divided into two groups for exploratory (n = 205) and confirmatory factor analysis (n = 206). Internal consistency was calculated using Cronbach’s alpha and McDonald’s omega coefficients. The analyses were performed using Jamovi software version 2.4.14 and Amos version 26. RESULTS: The exploratory factor analysis identified four factors of Attention, Expertise, Trustworthiness, and Information, explaining 56.3% of the total variance. Internal consistency coefficients ranged from 0.810 to 0.926. The extracted factors showed correlations above 0.50. Fit indices in the confirmatory factor analysis were appropriate (CMIN/DF = 2.427, CFI = 0.939, IFI = 0.939, NFI = 0.90, and RMSEA = 0.059). These results support the reliability and validity of the P-TIMHSS. CONCLUSION: The P-TIMHSS demonstrates strong psychometric properties, making it suitable for assessing trust in the Iranian healthcare system in national studies. Periodic assessments using this scale are recommended to identify trust deficits and design targeted interventions.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".