Building Trust and Trustworthiness in Public Institutions: Essential Elements in Placing Trust at the Heart of Health Policy and Systems Comment on "Placing Trust at the Heart of Health Policy and Systems"
Bibliographic record
Abstract
In this commentary, I argue that societies are facing major crises in trust that extends well beyond health systems, outlining actions that can enhance trust in public institutions and benefit health systems. There are also areas where strengthening health systems can serve to build broader trust and social cohesion, such as by providing social protection and health services that are responsive to people's needs. Understanding the dimensions of "trustworthiness" for different actors in a health system also provide insights on how to build, restore, and maintain trust. Whereas research evidence claims a foundational role for trustworthy intervention among health professions, other factors may be more influential for others. These include the credibility of the source, participation in the intervention with observably fair distribution of the benefits, the ethical behavior of key actors, reliability in service delivery and its results, transparent and consistent communications, and addressing breaches in trust.
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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 | 0.048 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.077 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.063 | 0.072 |
| 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".