General trust in the health care system and general trust in physicians: A multilevel analysis of 30 countries
Bibliographic record
Abstract
This article builds upon a multilevel theory of trust to explore the relationship between general trust in health care systems and general trust in physicians and the social-contextual factors that shape this relationship. We develop a model of trust in physicians emphasizing the embeddedness of individuals in broader social-institutional contexts. We analyze data from 30 countries in the 2011 International Social Survey Program ( N = 38,068) and specify hierarchical linear models with macro-micro level interactions. At the individual level, we find that individuals who trust the health care system are more likely to trust physicians in general. At the country level, we find that respondents from countries with predominately publicly financed health care systems are more likely to trust physicians than their counterparts in countries with less public funding of the health care system. Finally, we find that the greatest predicted probability of trust in physicians is found among individuals who trust their publicly funded health care system and the lowest probability is among individuals who have no confidence in their privately funded health care system. Based on these findings, we call for greater attention to the interaction of micro- and macro-level factors in models of trust in physicians cross-nationally.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".