Development and validation of the Trust in Multidimensional Healthcare Systems Scale (TIMHSS)
Bibliographic record
Abstract
Abstract Context The COVID-19 pandemic has reignited a commitment from the health policy and health services research communities to rebuilding trust in healthcare and created a renewed appetite for measures of trust for system monitoring and evaluation. The aim of the present paper was to develop a multidimensional measure of trust in healthcare that: (1) Is responsive to the conceptual and methodological limitations of existing measures; (2) Can be used to identify systemic explanations for lower levels of trust in equity-deserving populations; (3) Can be used to design and evaluate interventions aiming to (re)build trust. Methods We conducted a 2021 review of existing measures of trust in healthcare, 72 qualitative interviews (Aug-Dec 2021; oversampling for equity-deserving populations), an expert review consensus process (Oct 2021), and factor analyses and validation testing based on two waves of survey data (Nov 2021, n = 694; Jan-Feb 2022, n = 740 respectively). Findings We present the Trust in Multidimensional Healthcare Systems Scale (TIMHSS); a 38-item correlated three-factor measure of trust in doctors, policies, and the system. Measurement of invariance tests suggest that the TIMHSS can also be reliably administered to diverse populations. Conclusions This global measure of trust in healthcare can be used to measure trust over time at a population level, or used within specific subpopulations, to inform interventions to (re)build trust. It can also be used within a clinical setting to provide a stronger evidence base for associations between trust and therapeutic outcomes.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".