We surveyed authors of publications in high-impact psychiatry journals to assess the (1) proportion that disseminated results to study participants or others with lived experience, and, among those who disseminated, (2) methods (eg, email) and (3) tools (eg, plain-language summary) used.

Meta-research review.

PubMed search on 14 December 2022 and emails to study authors for information on dissemination.

Eligible studies collected primary human data and were published in psychiatry journals with 2021 impact factor ≥10.

Study information was extracted by one investigator and validated by a second investigator, with conflicts resolved by consensus, with a third investigator consulted as necessary. We emailed authors approximately 2 years post-publication to ensure sufficient time had passed to share results. We estimated the proportion of authors that may have disseminated results to participants or others with lived experience, assuming that non-respondents (1) did not disseminate, (2) were half as likely to disseminate as respondents or (3) disseminated in the same proportion as respondents.

Of 141 studies, 94 (67%) authors responded. Among respondents, 21 (22%) reported disseminating to study participants, and an additional 9 (10%) reported disseminating lay materials to people with lived experience (total of 30 studies, 32%). Overall, we estimated that 15% (95% CI 10% to 22%) to 23% (95% CI 17% to 30%) of authors may have disseminated results directly to study participants and 21% (95% CI 15% to 29%) to 32% (95% CI 25% to 40%) to participants or others with lived experience. Among the 30 that reported disseminating, the most common methods were sending mail or emails to study participants (17 studies, 57%) and posting on social media (15 studies, 50%). The most common tools were plain-language summaries (22 studies, 73%) and webinars or other meetings (15 studies, 50%).

Dissemination of results to participants in mental health research is uncommon. Funding agencies, ethics committees, journals and academic institutions should support dissemination.

To assess whether emergency physicians prescribe morphine differently for patients with or without sickle cell disease (SCD). Given the difficulty of comparing strictly homogeneous patients in real clinical settings, we used a standardised clinical vignette to ensure that all clinical information was identical except for SCD status and sex.

International, randomised controlled, vignette-based study conducted online. The four vignette versions differed only in patient sex and SCD status, with all other clinical information fully standardised. Vignettes were validated by an expert panel and randomly allocated using a computer-generated sequence.

Emergency physicians practising in France, the UK, Belgium and Switzerland were invited to complete an online survey between 17 February and 17 March 2025.

A total of 1060 physicians responded, of whom 953 (90%) met eligibility criteria and were included in the analysis. Respondents were practising emergency department (ED) physicians without exclusion based on seniority or training level.

The primary outcome was the proportion of simulated patients for whom morphine was prescribed. Secondary outcomes included the number and type of analgesics prescribed and the proportion of cases meeting predefined criteria for maximal level of care (urgent triage category, lactate sampling, CT imaging and morphine administration).

Morphine was prescribed in 444 of 492 (90%) SCD vignettes and 389 of 461 (84%) non-SCD vignettes (absolute difference: 6% (95% CI 1% to 10%)). Morphine monotherapy was used in 41% of SCD cases and combined analgesia in 50%. No significant differences were observed according to patient sex or physician characteristics. Maximal level of care was recommended in 22% of SCD cases.

In this randomised vignette study, emergency physicians prescribed morphine more frequently for simulated patients with SCD than for those without SCD, despite identical clinical presentations. These findings contrast with real-world reports of inadequate analgesia in SCD and suggest that the absence of perceptual cues—such as skin colour or names—may reduce implicit bias in opioid prescribing.

NCT06835335. IRB CHU Nîmes No 25.02.01.

Artificial intelligence (AI) in healthcare often requires large, confidential clinical datasets. However, a recent UK government survey revealed that 20–40% of the public remain sceptical of its use in health research due to concerns about data security, patient–practitioner communication and commercialisation of data. A greater understanding of public attitudes is therefore needed, particularly in the context of stroke research.

In this article, we describe the patient and public involvement work undertaken for the AI-Based-Stroke-Risk-fActor-Classification-and-Treatment (ABSTRACT) project, which aims to train AI models to predict future stroke risk from the electronic health records of 1 18 736 patients.

We aimed to evaluate the opinions of stroke/transient ischaemic attack (TIA) patients, caregivers and members of the public on the following themes: (1) the acceptability of using AI to predict stroke from electronic health records, (2) obtaining these data using an opt-out model of consent and (3) allowing access to this dataset from members both within and outside of the routine clinical care team.

A total of 83 participants were recruited via the National Health Service social media and by approaching hospital inpatients. Participants were first provided with background information on stroke, AI in medical research and ABSTRACT’s proposed data handling protocol. A mixed methods approach was then used to explore each of the above themes using online survey, semistructured focus groups and one-to-one interviews.

Nearly all participants felt that it was appropriate to use patient data to train AI models to predict stroke risk and that it was acceptable to obtain these data via an opt-out model of consent. Almost all participants also agreed that data could be shared within and outside of the routine clinical care team, provided it was General Data Protection Regulation compliant and used for medical research only.

The public and those with lived stroke/TIA experience appeared to support using deidentified medical datasets for AI-driven stroke risk prediction under an opt-out consent model. However, this is provided that the research conducted is transparent, for a clear medical purpose and adheres to strict data security measures.

To examine how clinicians’ scepticism regarding patients’ self-reports of subjective symptoms can be internalised, leading to psychosocial and medical harms.

In-depth, semi-structured qualitative interviews with the resulting data analysed using reflexive thematic analysis.

43 individuals with Ehlers-Danlos syndrome (EDS) from Europe and North America completed a pre-survey, and 39 of those participants completed interviews for this study. Purposive sampling was used to obtain approximately equal numbers of participants with hypermobile EDS and the molecularly defined types of EDS.

Patients with both hypermobile and molecularly defined types of EDS reported high levels of self-doubt, with 73% of survey respondents questioning the extent—and even reality—of their private experiences of pain. Participants attributed much of their self-doubt to repeated dismissal and minimisation of their symptoms in healthcare settings, especially during childhood. Ultimately, self-doubt transformed not merely how they communicated their symptoms but also how they recognised, evaluated and even experienced them at a phenomenological level. While some participants developed coping strategies, others withdrew from the conventional medical system altogether.

These findings have important implications for clinicians, who may inadvertently reinforce self-doubt through discussion of diagnostic uncertainty. Doubt need not be delegitamising. Recognising and mitigating these potential harms requires epistemic humility and attention to the psychosocial dynamics of patient-provider interaction.

The use of data science for health research produces complex ethical, legal and social challenges that traditional ethical oversight mechanisms struggle to address. In Nigeria, the current ethical guidelines were not designed for these challenges which include pervasive data environments, consent for secondary data use, algorithmic decision-making and bias, privacy risks, involvement of commercial entities, data colonisation, inequitable benefit-sharing and commercial data holdings. To address these gaps, we developed a draft guideline incorporating principles like trust, veracity, global justice and alternative ethical approval mechanisms. Here, we describe the protocol for a study aimed at validating the guideline through stakeholder consensus on the content, feasibility and acceptability of this subcode for national implementation.

We describe the use of a modified e-Delphi approach to iteratively synthesize expert opinions about ethical oversight for data science health research (DSHR) led by a multidisciplinary working group from the Bridging Gaps in the ELSI of Data Science Health Research in Nigeria (BridgELSI) team. We will invite 65 experts, including health researchers, ethics committee members, data scientists, health policymakers, funders and key opinion leaders in Nigeria to participate. Participants will rate 13 core principles, including global justice, algorithmic bias, data governance and related governance provisions on importance, desirability for inclusion in national guidelines, feasibility and confidence in implementation, using 5-point Likert scales, with optional free-text comments. We will summarise responses using descriptive statistics, assess consensus and polarity using pre-specified thresholds for the mean and IQR, and iteratively refine statements between rounds using qualitative content analysis of comments.

Ethical approval was obtained from the Nigerian National Health Research Ethics Committee and the University of Maryland IRB, and participants will provide informed consent. Results will be shared with the expert panel and national regulators and disseminated via publications and conferences.

Moral distress is a significant challenge in contemporary nursing practice, posing a substantial threat to nurses’ well-being and patient safety. Nurses in the emergency department are considered a high-risk group for experiencing this distress due to their unique working environment. Although numerous qualitative studies have explored this issue, a systematic synthesis of this fragmented evidence is notably absent. This qualitative meta-synthesis aims to integrate existing evidence to construct a comprehensive conceptual framework of the experiences, processes and coping mechanisms related to moral distress among emergency nurses.

This study will be a qualitative systematic review and meta-synthesis, adhering to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols statement and the Joanna Briggs Institute (JBI) methodology. A systematic search will be conducted in international and Chinese databases, including PubMed, CINAHL, Embase, CNKI, etc. All peer-reviewed qualitative studies exploring the first-person experiences of emergency nurses will be included. Two independent reviewers will perform study selection, data extraction and methodological quality appraisal using the JBI Critical Appraisal Checklist for Qualitative Research. Data synthesis will employ a theory-integrated meta-aggregation approach, systematically mapping findings onto the Stress, Appraisal, and Coping Theory to construct a nuanced conceptual framework that explains the dynamic process of moral distress. Confidence in the synthesised findings will be assessed using the ConQual approach.

As this study is a secondary analysis of published data, ethical approval is not required. The findings will be disseminated through publication in a peer-reviewed journal and presentations at academic conferences.

CRD420251041396.

The study aims to define the prevalence of Do-Not-Resuscitate (DNR) orders among patients with shock in the emergency department (ED) and explore their impact on clinical management and mortality outcomes.

A retrospective observational cohort study was conducted involving patients presenting to the ED with shock.

An ED in a tertiary hospital in western China.

2001 patients (aged ≥18 years) presenting to the ED with shock from 1 January 2022 to 31 December 2023.

The enrolled patients were divided into DNR (order issued within 24 hours of ED admission)/non-DNR groups. Demographics, vitals, comorbidities, laboratory values, medications and prognoses were obtained from electronic healthcare records. DNR prevalence and its associations with mortality, ICU admission, vasopressor administration and antibiotic administration were assessed via logistic regression.

Compared with patients without DNR orders, patients with DNR orders (n=399 (19.9%)) were older (p

Compared with patients with shock in the ED who did not have DNR status, those with DNR status (prevalence ~20%) had higher in-hospital and 30-day mortality (but most survived) and similar ICU admission and intervention treatments.