ArticleJMIR medical education2026
Trust Analysis Canvas for Teaching in the Field of Digital Public Health and Medicine: Tutorial.
Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Conditional Acceptance and the Optimism-Knowledge Gap: A Scoping Review of Attitudes and Perceptions of Artificial Intelligence in Healthcare in Italy.Medical sciences (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: Trust is increasingly recognized as a cornerstone for the successful implementation of digital public health initiatives, from mobile apps to the use of artificial intelligence in medicine, yet it remains underrepresented in educational curricula. In the course of our research and teaching activities in the field of trust in digital public health and medicine, we identified a gap in existing educational resources that aimed at supporting students in conducting structured trust analyses. Digitalization introduces new complexities into trust relationships, as interactions become increasingly mediated by digital tools. Preparing future professionals, therefore, demands fostering a critical understanding of how trust operates within digital systems, especially in the health sector. To address this gap, we developed and tested the first Trust Analysis Canvas for Teaching (TACT), a tool designed to guide students in conducting trust analyses of case studies in digital public health and medicine. Grounded in conceptual research on trust in health systems and health data sharing, we (1) developed the canvas content and reviewed it with two trust researchers; (2) tested and iteratively refined the tool with 23 students (3 BSc, 14 MSc, and 6 PhD) from diverse disciplines and academic levels through in-person and online focus groups at the universities of Zurich and Bern; (3) collaborated with a graphic designer to optimize its visual layout; and (4) translated the final canvas into French, Italian, German, and Spanish to ensure accessibility across disciplines, academic levels, and languages while maintaining a clear and engaging visual design. This paper introduces TACT, a canvas comprising 16 guiding questions organized around 6 core dimensions, designed to enable students from diverse disciplinary backgrounds and academic levels to engage with the complex concept of trust in a structured and guided manner, thereby addressing the identified gap in the current curricula. We outline the development process and provide a practical, step-by-step tutorial demonstrating its application through a written trust analysis of a digital health case study, supported by references to relevant literature.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.