BACKGROUND: Suicide is a leading cause of death worldwide. Journalistic reporting guidelines were created to curb the impact of unsafe reporting; however, how suicide is framed in news reports may differ by important characteristics such as the circumstances and the decedent\'s gender.
OBJECTIVE: This study aimed to examine the degree to which news media reports of suicides are framed using stigmatized or glorified language and differences in such framing by gender and circumstance of suicide.
METHODS: We analyzed 200 news articles regarding suicides and applied the validated Stigma of Suicide Scale to identify stigmatized and glorified language. We assessed linguistic similarity with 2 widely used metrics, cosine similarity and mutual information scores, using a machine learning-based large language model.
RESULTS: News reports of male suicides were framed more similarly to stigmatizing (P<.001) and glorifying (P=.005) language than reports of female suicides. Considering the circumstances of suicide, mutual information scores indicated that differences in the use of stigmatizing or glorifying language by gender were most pronounced for articles attributing legal (0.155), relationship (0.268), or mental health problems (0.251) as the cause.
CONCLUSIONS: Linguistic differences, by gender, in stigmatizing or glorifying language when reporting suicide may exacerbate suicide disparities.

BACKGROUND: Despite the improvements in the completeness of reporting of randomized trial protocols after the publication of the Standard Protocol Items: Recommendations for Interventional Trial (SPIRIT) guidelines, many items remain poorly reported. This study aimed to assess the effectiveness of using SPIRIT-tailored templates for trial protocols to improve the completeness of reporting of the protocols that master\'s students write as part of their master\'s theses.
METHODS: Before and after experimental study performed at the University Master\'s Degree in Orthopaedic Manual Physiotherapy of the Universitat Internacional de Catalunya (Barcelona, Spain). While students in the post-intervention period were instructed to use a trial protocol template that was tailored to SPIRIT, students in the pre-intervention period did not use the template.
METHODS: Difference between the pre- and post-intervention periods in the mean number of adequately reported items (0-10 scale). The outcomes were evaluated independently and in duplicate by two blinded assessors. Students and their supervisors were not aware that they were part of a research project. For the statistical analysis, we used a generalized linear regression model (dependent variable: number of adequately reported items in the protocol; independent variables: intervention period, call, language).
RESULTS: Thirty-four trial protocols were included (17, pre-intervention; 17, post-intervention). Protocols produced during the post-intervention period (mean: 8.24; SD: 1.52) were more completely reported than those produced during the pre-intervention period (mean: 6.35; SD: 1.80); adjusted difference: 1.79 (95% CI: 0.58 to 3.00).
CONCLUSIONS: SPIRIT-based templates could be used to improve the completeness of reporting of randomized trial protocols.

The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for example, avoid instances of missing information. In this Editorial, the editors of JMIR Publications journals discuss the general JMIR Publications policy regarding authors\' application of reporting guidelines and specifically focus on the reporting of ML studies in JMIR Publications journals, using the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines, with an example of how authors and other journals could use the CREMLS checklist to ensure transparency and rigor in reporting.

UNASSIGNED: To develop text classification models for determining whether the checklist items in the CONSORT reporting guidelines are reported in randomized controlled trial publications.
UNASSIGNED: Using a corpus annotated at the sentence level with 37 fine-grained CONSORT items, we trained several sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. To address the problem of small training dataset, we used several data augmentation methods (EDA, UMLS-EDA, text generation and rephrasing with GPT-4) and assessed their impact on the fine-tuned PubMedBERT model. We also fine-tuned PubMedBERT models limited to checklist items associated with specific sections (e.g., Methods) to evaluate whether such models could improve performance compared to the single full model. We performed 5-fold cross-validation and report precision, recall, F1 score, and area under curve (AUC).
UNASSIGNED: Fine-tuned PubMedBERT model that takes as input the sentence and the surrounding sentence representations and uses section headers yielded the best overall performance (0.71 micro-F1, 0.64 macro-F1). Data augmentation had limited positive effect, UMLS-EDA yielding slightly better results than data augmentation using GPT-4. BioGPT fine-tuning and GPT-4 in-context learning exhibited suboptimal results. Methods-specific model yielded higher performance for methodology items, other section-specific models did not have significant impact.
UNASSIGNED: Most CONSORT checklist items can be recognized reasonably well with the fine-tuned PubMedBERT model but there is room for improvement. Improved models can underpin the journal editorial workflows and CONSORT adherence checks and can help authors in improving the reporting quality and completeness of their manuscripts.

UNASSIGNED: Digital mental health interventions (DMHIs) are an effective and accessible means of addressing the unprecedented levels of mental illness worldwide. Currently, however, patient engagement with DMHIs in real-world settings is often insufficient to see clinical benefit. In order to realize the potential of DMHIs, there is a need to better understand what drives patient engagement.
UNASSIGNED: We discuss takeaways from the existing literature related to patient engagement with DMHIs and highlight gaps to be addressed through further research. Findings suggest that engagement is influenced by patient-, intervention- and systems-level factors. At the patient-level, variables such as sex, education, personality traits, race, ethnicity, age and symptom severity appear to be associated with engagement. At the intervention-level, integrating human support, gamification, financial incentives and persuasive technology features may improve engagement. Finally, although systems-level factors have not been widely explored, the existing evidence suggests that achieving engagement will require addressing organizational and social barriers and drawing on the field of implementation science.
UNASSIGNED: Future research clarifying the patient-, intervention- and systems-level factors that drive engagement will be essential. Additionally, to facilitate improved understanding of DMHI engagement, we propose the following: (a) widespread adoption of a minimum necessary 5-element engagement reporting framework; (b) broader application of alternative clinical trial designs; and (c) directed efforts to build upon an initial parsimonious conceptual model of DMHI engagement.

UNASSIGNED: News media reporting of suicide may influence the masses toward or against suicidal behavior. So, responsible reporting by news media is important to protect vulnerable people from the deleterious effects of media reporting. World Health Organization has issued guidelines for media reporting of suicide in this regard. We planned this research to evaluate the quality of online news media reports of suicide and attempted suicides in India.
UNASSIGNED: We assessed a total of 210 news reports about suicide and attempted suicides (70 English, 70 Malayalam, and 70 Hindi) to evaluate the reporting of harmful and protective characteristics in news according to the World Health Organization guidelines for reporting suicide in media.
UNASSIGNED: We found all three language media reported almost all potentially harmful characteristics with gender (100%), location/site of suicide in the headline (97.61%), precipitating life events (95.24%), mentioning term \'Suicide\' in the headline (98.10%), monocausal explanation for suicidality (90.95%), suicide method in the headline (90.95%), an accompanying photo (92.38%), and suicide method (93.81%) being reported with highest frequency. Potentially protective characteristics were not included in most news reports with poor focus on awareness information. Vernacular language media were poor as compared to English news media about the quality of reporting suicide.
UNASSIGNED: We found poor adherence to reporting guidelines by online news media for reporting of suicide with poor coverage of educative and awareness information for the general public. It underlines the need for improving awareness among media professionals about responsible reporting of suicide.

A set of guidelines has been developed to help improve reporting of clinical trials of biofield therapies. The need for enhanced transparency when reporting trials of this family of integrative health practices, eg, External Qigong, Healing Touch, Reiki and Therapeutic Touch, has been advocated in systematic reviews of these studies. The guidelines, called Biofield Therapies: Reporting Evidence Guidelines (BiFi REGs), supplement CONsolidated Standards of Reporting Trials (CONSORT) 2010 by including details of the intervention protocols relevant to biofield therapy trials. BiFi REGs evolved through a draft document created by a core group, two rounds of a Delphi process with an international group of subject matter experts and two panels, meeting via Zoom, which included editors of complementary and integrative medicine journals. BiFi REGs comprises a 15-item Intervention checklist. Modifications of two other CONSORT topic areas are also proposed to enhance their relevance to trials of biofield therapies. Included for each item are an explanation, and exemplars of reporting from peer-reviewed published reports of biofield therapy trials. When used in conjunction with all other items from CONSORT 2010, we anticipate that BiFi REGs will expedite the peer review process for biofield therapy trials, facilitate attempts at trial replication and help to inform decision-making in the clinical practice of biofield therapies.

Highlights Guidelines have been created to improve the reporting of clinical trials of biofield therapies, e.g. External Qigong, Healing Touch, Reiki, and Therapeutic Touch. Appropriate use of these guidelines is likely to strengthen the evidence base for biofield therapies as well as increase their usage as stand-alone practices and as complementary therapies within mainstream healthcare.

The Consensus Reporting Items for Studies in Primary care (CRISP) provides a new research reporting guideline to meet the needs of the producers and users of primary care (PC) research. Developed through an iterative program of research, including investigators, practicing clinicians, patients, community representatives, and educators, the CRISP Checklist guides PC researchers across the spectrum of research methods, study designs, and topics. This pilot test included a variety of team members using the CRISP Checklist for writing, revising, and reviewing PC research reports. All or most of the 15 participants reported that the checklist was easy to use, improved research reports, and should be recommended by PC research journals. The checklist is adaptable to different study types; not all items apply to all reports. The CRISP Checklist can help meet the needs of PC research when used in parallel with existing guidelines that focus on specific methods and limited topics.

BACKGROUND: There is no consensus on reporting light characteristics in studies investigating non-visual responses to light. This project aimed to develop a reporting checklist for laboratory-based investigations on the impact of light on non-visual physiology.
METHODS: A four-step modified Delphi process (three questionnaire-based feedback rounds and one face-to-face group discussion) involving international experts was conducted to reach consensus on the items to be included in the checklist. Following the consensus process, the resulting checklist was tested in a pilot phase with independent experts.
RESULTS: An initial list of 61 items related to reporting light-based interventions was condensed to a final checklist containing 25 items, based upon consensus among experts (final n = 60). Nine items were deemed necessary to report regardless of research question or context. A description of each item is provided in the accompanying Explanation and Elaboration (E&E) document. The independent pilot testing phase led to minor textual clarifications in the checklist and E&E document.
CONCLUSIONS: The ENLIGHT Checklist is the first consensus-based checklist for documenting and reporting ocular light-based interventions for human studies. The implementation of the checklist will enhance the impact of light-based research by ensuring comprehensive documentation, enhancing reproducibility, and enabling data aggregation across studies.
BACKGROUND: Network of European Institutes for Advanced Study (NETIAS) Constructive Advanced Thinking (CAT) programme; Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust, 204686/Z/16/Z); Netherlands Organisation for Health Research and Development VENI fellowship (2020-09150161910128); U.S. Department of Defense Grant (W81XWH-16-1-0223); National University of Singapore (NUHSRO/2022/038/Startup/08); and National Research Foundation Singapore (NRF2022-THE004-0002).