Development and validation of the S-TIMHSS: a quality metric to inform and evaluate interventions to (re)build trust
Bibliographic record
Abstract
Introduction: Public acceptance of health messaging, recommendations, and policy is heavily dependent on the public's trust in doctors, health systems and health policy. Any erosion of public trust in these domains is thus a concern for public health as it can no longer be assumed that the public will follow official health recommendations. In response, the health policy and health services communities have emphasized a commitment to (re)building trust in healthcare. As such, measures of trust that can be used to develop and evaluate interventions to (re)build trust are highly valuable. In 2024, the Trust in Multidimensional Health System Scale (TIMHSS) was published, providing the first measure of trust in healthcare that includes doctors, the system and health policy within a single measure. This measure can effectively facilitate research on trust across diverse populations. However, it is limited in its application because results cannot be directly added together for a total trust score. Further, at 38-items, it is burdensome for respondents and analysts, particularly when being used as a repeat measure in an applied setting. The aim of the present work was to develop a shortened measure of trust in healthcare for use in applied settings. Methods: = 512; in Sept 2024) to reduce the number of items and to test if the factor structure was consistent with the original TIMHSS. Several statistical criteria were used to support item reduction (i.e., correlated errors, measurement invariance, inter-item correlations, factor loadings and communalities, item-total correlation, and skewness), as well as an exercise testing the content validity ratio (CVR). We then tested a three-factor model based on the 18 items that remained following the CVR and statistical test metrices to finalize the measure. Results: The S-TIMHSS is an 18-item scale that allows for direct scoring of trust items for applied research. It preserves the content, convergent, and criterion validity of the original 38-item version. Discussion: We recommend the measure be used by health policy makers and practitioners as a quality metric to inform and evaluate interventions which aim to (re)build trust in doctors, health systems and health policy.
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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 | 0.075 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".