Development and validation of the Trust in Government measure (TGM)
Bibliographic record
Abstract
BACKGROUND: Trust in government is associated with health behaviours and is an important consideration in population health interventions. While there is a reported decline in public trust in government across OECD countries, the tools used to measure trust are limited in their use for informing action to (re)build trust, and have limitations related to reliability and validity. To address the limitations of existing measures available to track public trust, the aim of the present work was to develop a new measure of trust in government. METHODS: Fifty-six qualitative interviews (Aug-Oct 2021; oversampling for equity-deserving populations) were conducted to design a national survey, including factor analyses and validation testing (N = 878; June 1-14th 2022) in Canada. RESULTS: The measure demonstrated strong internal consistency (α = 0.96) and test validity (CFI = 0.96, RMSEA = 0.09, SRMR = 0.03), suggesting that trust in government can be measured as a single underlying construct. It also demonstrated strong criterion validity, as measured by significant (p < 0.0001) associations of scores with vaccine hesitancy, vaccine conspiracy beliefs, COVID-19 conspiracy beliefs, trust in public health messaging about COVID-19, and trust in public health advice about COVID-19. We present the Trust in Government Measure (TGM); a 13-item unidimensional measure of trust in Federal government. CONCLUSIONS: This measure can be used within high-income countries, particularly member countries within the OECD already in support of using tools to collect, publish and compare statistics. Our measure should be used by researchers and policy makers to measure trust in government as a key indicator of societal and public health.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".