Participatory action research (PAR) is a collaborative research approach that engages community members as equal partners to generate knowledge and drive meaningful change. Though common in healthcare, it remains relatively new in medical education. This paper uses a collaborative approach to synthesize a practical framework for using PAR among medical education researchers. The paper offers 12 practical tips covering critical steps: tip 1: Know the distinct boundaries between AR and PAR, Tip 2: Practice continuous positionality and reflexivity, Tip 3: Address ethical considerations, Tip 4: Build trusting relationships with community members before collecting data, Tip 5: Embed individual PAR projects within long-term partnerships, Tip 6: Identify the problem you can address with PAR, and co-create specific research questions, Tip 7: Take action to produce social change, Tip 8: Provide accessible research capacity building, Tip 9: Use different methodological approaches to collect data, Tip 10: Analyze collaboratively, reflect, and redefine the problem, Tip 11: Avoid using jargon in your research language, and Tip 12: Ensure participants are part of the dissemination process. PAR offers medical education researchers a rigorous approach to partnering with communities to address locally relevant challenges and translate research into meaningful action.

The paper reflects on a Wellcome-funded project led by the Medicines and Healthcare products Regulatory Agency (MHRA) in collaboration with the National Institute for Health and Care Excellence (NICE). Initiated in 2023, the project aimed at developing guidance and regulatory frameworks for digital mental health technologies (DMHTs), emphasising meaningful lived experience involvement throughout the process to ensure accessibility, inclusivity, and accountability. It details methods of engagement, key insights on best practices for lived experience involvement, challenges such as overcoming technical jargon and provides recommendations for broader adoption of lived experience integration, improved public awareness, cross-sector collaboration, and equitable digital futures in mental health regulation and innovation. The paper offers a hybrid narrative approach in which the details of the project are presented and are followed by the lived experience insights of the contributing authors. This highlights the collaborative nature of the project itself and the outputs but gives space for the considered reflections of what it felt like to be part of this initiative. The paper considers recommendations for how lived experience should be incorporated into similar projects in future. The paper articulates the benefit of lived experience for projects that have an impact on the way in which DMHTs are regulated. In this instance the outputs, informed by lived experience, help ensure that DMHTs are safe and effective and will be reflected in what technologies are integrated into mental health nursing practice. It is also important to note that DMHTs are often accessed outside of mental health service settings, and so practitioner awareness of their risks and benefits should be encouraged.

To increase the uptake of preventive healthcare services in the elderly, Medicare implemented the Annual Wellness Visit (AWV). Many patients are unaware of this free annual visit so uptake of the AWV in primary care has been low. Boot Camp Translation (BCT) is an evidence-based method for translating complex medical jargon into messages and materials for patients and primary care practices.

This article is a viewpoint: it presents the authors' perspective, informed by a critical synthesis of the current literature at the intersection of generative AI (GenAI) technologies, public health communication, and digital ethics, rather than original empirical data or analyses. The emergence of GenAI, including large language models (LLMs), represents a profound paradigm shift in digital health communication. By moving beyond traditional information retrieval to dynamic, human-like knowledge generation, GenAI offers unprecedented opportunities for public health promotion. However, the unguided integration of these powerful commercial models into health care systems poses profound sociotechnical risks. In this viewpoint, we aim to communicate three key messages to public health researchers, practitioners, policymakers, and AI developers: (1) GenAI offers transformative applications for public health promotion, spanning personalized health education, stigma mitigation, and accelerated epidemiological surveillance; (2) the unguided integration of commercial generative models simultaneously generates intersecting sociotechnical risks and ethical challenges, encompassing a widening "AI digital divide," algorithmic bias and epistemic opacity, and the erosion of data privacy and governance; and (3) an equity-centered sociotechnical architecture, built on 4 strategic pillars, is required to govern this transition safely. We conducted a critical synthesis of the current literature and theoretical frameworks at the intersection of GenAI technologies, public health communication, and digital ethics, systematically mapping both the translational capabilities and the sociotechnical vulnerabilities of generative models. GenAI demonstrates transformative potential across 3 primary domains: democratizing health education by translating complex medical jargon, mitigating societal stigma through nonjudgmental conversational interfaces, and accelerating epidemiological surveillance via rapid thematic synthesis. However, these benefits are counterbalanced by a matrix of sociotechnical risks. Specifically, unguided GenAI deployment threatens to exacerbate a novel "AI digital divide" driven by economic exclusion, prompt literacy demands, and linguistic biases; compromise clinical safety through deep-seated algorithmic biases and epistemic opacity; and erode patient privacy through profound vulnerabilities in cybersecurity and corporate data governance. The advent of GenAI marked an irreversible paradigm shift with the unprecedented capacity to democratize health literacy, dismantle stigma, and accelerate disease surveillance. However, treating GenAI as a technological panacea is a perilous oversight. Without intentional, equity-focused interventions, these technologies invariably scale and automate the structural inequalities they have the potential to solve. Ultimately, the future of digital health promotion depends not only on the computational power of these models but also on the ethical, regulatory, and inclusive sociotechnical architectures we design to govern them.

Radiology reports are often filled with medical jargon that limits patient understanding. Lay summaries can improve understanding but are time-consuming for healthcare providers to create. The objective of this study is to explore the use of tailored prompts for five Large Language Models (LLMs) in generating lay summaries from radiology reports. Using 100 reports from the publicly available "BioNLP 2023 report summarization" dataset, lay summaries were generated by each LLM, under select prompting styles [Few-Shot (GPT-4), Generated Knowledge (GPT-4o mini, Gemini 1.5 - Pro, Gemini 1.5 - Flash), and Zero-Shot (Llama 3.1)] informed by a pilot work. The summaries were evaluated using a mixed-method framework: subjective assessment (Likert statements) by blinded experts (n = 2 radiology fellows) and Large Reasoning Models (LRMs) [(Gemini 2.5 - Pro (LRM 1); GPT-oss-120b (LRM 2)], and readability metrics (Flesch-Kincaid Grade Level and Flesch Reading Ease). Using percentage agreement of Likert statements, the LLM-prompt combinations' performances were ranked, and Friedman and post-hoc Nemenyi tests were conducted. Gemini 1.5 - Flash and - Pro (generated knowledge) were rated highest by human experts and LRMs for generating actionable lay summaries that require minimal supervision [P < 4.97 × 10-2 (Rater 1); P < 9.03 × 10-21 (Rater 2), P < 6.90 × 10-15 (LRM 1), P < 2.760 × 10-5 (LRM 2). GPT-4 (few-shot) achieved the highest human-rated accuracy (98%), while Gemini 1.5 - Flash (LRM 1-rated: 95%) and Gemini 1.5 - Pro (LRM 2-rated: 91%) ranked first in LRM-rated accuracy. Gemini 1.5 - Pro produced the most accessible summaries (Flesch-Kincaid Grade Level: 7.55 ± 1.38, Flesch Reading Ease: 67.84 ± 7.78). Strong agreement was observed between experts and LRMs [0.96% (LRM 1) and 3.4% (LRM 2) complete disagreement]. Overall, this study highlights Gemini-models with generated knowledge prompts and the potential of LRM evaluators in assessing LLM-generated lay summaries.

Colorectal cancer (CRC) remains a leading cause of preventable cancer mortality, with persistent screening disparities in underserved communities. This qualitative study aimed to identify community-specific barriers to CRC screening among two distinct Maryland populations to inform the development of tailored educational materials. We conducted four in-person focus groups with 24 adults, consisting of residents from Baltimore City (BC) and the rural Maryland Eastern Shore (ES). Semi-structured guides explored CRC screening knowledge, perceived barriers, trusted information sources, and preferences for educational content. Discussions were audio-recorded, transcribed, and analyzed by two researchers using thematic analysis. There were significant differences between screening barriers in both populations. In the BC population, key barriers included lack of CRC symptom knowledge, stigma surrounding colorectal procedures, lack of recommendation for screening, and use of medical jargon during appointments. Participants emphasized the importance of plain language, visual aids, and community-specific references to increase CRC screening. In the ES population, key barriers included disengagement with healthcare, not pursuing medical care outside of emergencies, and significant logistical challenges. Participants emphasized the importance of incentives to encourage involvement with the healthcare system as well as using community health workers to help decrease logistic barriers to CRC screening. Within the state of Maryland, there are significantly different CRC screening barriers among BC and ES populations. This study elucidates the value of community-engaged approaches in developing CRC screening educational materials. These findings provide a foundation for future interventions and health education in similar underserved settings.

Covert signaling describes communication that is understood by an intended sub-audience while remaining obscured to others. In the healthcare lexicon, the term "pan-positive review of systems" can be used as such to convey veiled doubt in a patient's story, introducing potential bias that can risk patient relationships and clinical care.

Investigated relations among report characteristics, feedback impact, and parent understanding of the child and report after comprehensive assessment and feedback. Fifty-five parents/guardians (X¯age = 39.35[SD = 8.80]) of children (X¯age = 8.69[SD = 3.86]) referred for outpatient assessment completed a follow-up survey. Most parents (>80.0%) reported having read each report section. Diagnosis and Recommendations were considered the most important. Excluding the Appendix, average self-reported report understanding using a 5-point Likert scale was ≥4.15, with negatively skewed distributions. Child understanding measures were correlated with report understanding and feedback components; report characteristics were not. Feedback impact measures were significant in robust linear regressions predicting child understanding. In open-ended questions, diagnoses were recalled accurately by 52.7% of respondents. Approximately 40% accurately described percentile rank. While 43.4% reported an Individualized Education Program (IEP) was associated with school services, 42.4% did not know the difference between an IEP and 504 Plan. Results suggest that the feedback interaction is more important to child understanding vis-à-vis the written report. Furthermore, despite high self-reported report (and child) understanding, parents were less-than-ideal reporters of diagnoses. Many parents did not fully understand specific jargon words utilized in results and recommendations. These results have practical implications regarding report writing practices and parents' ability to advocate for their children.

The present study aims to identify the health literacy demands, organized based on their different domains, present in the second edition of the Dietary Guidelines for the Brazilian Population (2014). It is an exploratory-descriptive study, based on the application of a protocol, previously validated for the national context, for health literacy load analysis of the material, with subsequent content analysis of selected excerpts, organized in a matrix. The analysis of the results identified possible barriers to understanding and make meaning of the Guide recommendations, organized according to the fundamental, scientific, civic, and cultural domains of health literacy, which ranged from the use of non-colloquial language and jargon to the impossibility of following the recommendations present therein, due to the context of the social group. The results point to the need to bring the form and content of the material closer to the health literacy demands of its potential beneficiaries, facilitating access, understanding, evaluation and significance of its recommendations by health workers and groups of the population for whom it is intended.