How do you measure trust in social institutions and health professionals? A systematic review of the literature (2012–2021)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it