How do you measure trust in social institutions and health professionals? A systematic review of the literature (2012–2021)
Bibliographic record
Abstract
Abstract The importance of measuring trust in health systems has been accentuated due to its correlation with important health outcomes aimed at reducing COVID‐19 transmission. A systematic review published almost a decade ago identified gaps in measures including the lack of focus on trust in systems, inconsistency regarding the dimensionality of trust and need for research to strengthen the validity of measures. Given developments in our understandings of trust since its publication, we sought to identify new scales developed, existing ones adapted in response to identified gaps, and agendas for future research. Using the PRISMA approach for systematic reviews, we conducted a search in four databases. A total of 26 articles were assessed. Twelve new scales were identified, while 14 were adapted for different settings and populations. Literature continues to focus on measuring trust in health professionals rather than systems. Various shortcomings were identified, including some articles not mentioning the dimensions included in the scale and suboptimal use of validity and reliability testing and/or reporting. Moreover, a variety of terms were used for dimensions. Future research is needed to address these gaps and consequently, to understand their correlation with health behaviors and outcomes more accurately.
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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 | 0.029 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".