Search

Since the World Health Organization declared coronavirus disease (COVID-19) a pandemic on March 11, 2020 (1), COVID-19 has infiltrated every continent in the world (2), Hong Kong, a densely populated [...]

Large language models (LLMs) such as ChatGPT have emerged as potential game-changers in nursing, aiding in patient education, diagnostic assistance, treatment recommendations, and administrative task efficiency. While these advancements signal promising strides in healthcare, integrated LLMs are not without challenges, particularly artificial intelligence hallucination and data privacy concerns. Methodologies such as prompt engineering, temperature adjustments, model fine-tuning, and local deployment are proposed to refine the accuracy of LLMs and ensure data security. While LLMs offer transformative potential, it is imperative to acknowledge that they cannot substitute the intricate expertise of human professionals in the clinical field, advocating for a synergistic approach in patient care. [ABSTRACT FROM AUTHOR]

Hong Kong was relatively successful in mitigating transmission early in the outbreak of coronavirus disease (COVID-19). Confirmed cases were first reported in the city of Wuhan, China, in December 2019 [...]

The integration of generative artificial intelligence (AI) into academic research writing has revolutionized the field, offering powerful tools like ChatGPT and Bard to aid researchers in content generation and idea enhancement. We explore the current state of transparency regarding generative AI use in nursing academic research journals, emphasizing the need for explicitly declaring the use of generative AI by authors in the manuscript. Out of 125 nursing studies journals, 37.6% required explicit statements about generative AI use in their authors' guidelines. No significant differences in impact factors or journal categories were found between journals with and without such requirement. A similar evaluation of medicine, general and internal journals showed a lower percentage (14.5%) including the information about generative AI usage. Declaring generative AI tool usage is crucial for maintaining the transparency and credibility in academic writing. Additionally, extending the requirement for AI usage declarations to journal reviewers can enhance the quality of peer review and combat predatory journals in the academic publishing landscape. Our study highlights the need for active participation from nursing researchers in discussions surrounding standardization of generative AI declaration in academic research writing. [ABSTRACT FROM AUTHOR]

The recurrent multiwave nature of coronavirus disease 2019 (COVID‐19) necessitates updating its symptomatology. We characterize the effect of variants on symptom presentation, identify the symptoms predictive and protective of death, and quantify the effect of vaccination on symptom development. With the COVID‐19 cases reported up to August 25, 2022 in Hong Kong, an iterative multitier text‐matching algorithm was developed to identify symptoms from free text. Multivariate regression was used to measure associations between variants, symptom development, death, and vaccination status. A least absolute shrinkage and selection operator technique was used to identify a parsimonious set of symptoms jointly associated with death. Overall, 70.9% (54 450/76 762) of cases were symptomatic with 102 symptoms identified. Intrinsically, the wild‐type and delta variant caused similar symptoms among unvaccinated symptomatic cases, whereas the wild‐type and omicron BA.2 subvariant had heterogeneous patterns, with seven symptoms (fatigue, fever, chest pain, runny nose, sputum production, nausea/vomiting, and sore throat) more frequent in the BA.2 cohort. With ≥2 vaccine doses, BA.2 was more likely than delta to cause fever among symptomatic cases. Fever, blocked nose, pneumonia, and shortness of breath remained jointly predictive of death among unvaccinated symptomatic elderly in the wild‐type‐to‐omicron transition. Number of vaccine doses required for reducing occurrence varied by symptoms. We substantiate that omicron has a different clinical presentation compared to previous variants. Syndromic surveillance can be bettered with reduced reliance on symptom‐based case identification, increased weighing on symptoms predictive of death in outcome prediction, individual‐based risk assessment in care homes, and incorporating free‐text symptom reporting. [ABSTRACT FROM AUTHOR]

To inform the dynamic adjustments of vaccination campaigns, this study examined the transitions among vaccine hesitancy profiles over the COVID‐19 pandemic progression and their predictors and outcomes. The transition patterns among hesitancy profiles over three periods were identified using a latent transition analysis with individuals from a longitudinal cohort study since the emergence of COVID‐19 in Hong Kong. Four profiles (i.e., skeptics, apathetics, fence‐sitters, and believers) emerged consistently over time. From Period 1 (third and fourth pandemic waves) to Period 2 (dormant period, vaccine rollout), 14.17% of believers became fence‐sitters (ambivalization), and 12.11% of fence‐sitters became apathetics (apathetization). From Period 2 to Period 3 (omicron surge and vaccine mandates), 20.21% of believers became fence‐sitters. Lower trust in government predicted a transition to skepticism, whereas higher trust predicted the opposite. Staying as believers was associated with decreased hygienic and social distancing behavior. The stable hesitancy profiles amid the rapid vaccine uptake suggest that structural factors rather than personal agency may drive the surge. Ambivalization and apathetization may signal disengagement in preventive behaviors. Trust in the government is crucial in the pandemic response. Public health interventions may improve compliance with guidelines and prevent skepticism and apathy. [ABSTRACT FROM AUTHOR]

Suicide notes have been regarded as one of the most informative data sources to understand the reasons why people commit suicide. However, there is a paucity of suicide note studies, leaving researchers with an assumption that this phenomenon remains static over time. This study examines this assumption by comparing the characteristics of note-leavers of the same population at 2 different points in time. The authors investigated the characteristics of 316 note-leavers in 2000 in Hong Kong and compared them to note-leavers in 1992. The proportion of people who left suicide notes increased from 20% to 35% between 1992 and 2000, a change that may be related to changes in suicide methods. Note-leavers in 2000 were characterized as individuals aged 25-59 years old, employed, with no physical illness, no psychiatric illness, and died as a result of charcoal-burning. Present data suggest that reasons for leaving suicide notes may have changed over the 8 years from 1992 to 2000. (Contains 1 table.)

Introduction: Research impact and influence are commonly measured quantitatively by citation count received by research articles. Many institutes also use citation count as one of the factors in faculty performance appraisal and candidate selection of academic positions. Various strategies were recommended to amplify and accelerate research influence, particularly citation counts, by bringing research articles to a wider reach for potential readers. However, no prior empirical study was conducted to examine and valid effects of those strategies on nursing studies. This study examines and verifies the direct effects and mediation effects of some strategies, namely, the use of Twitter, international collaboration, the use of ResearchGate, and open access publishing, for amplifying the citation of research and review articles in nursing studies. Design: Cross‐sectional study design. Methods: Articles published in top nursing journals in 2016 were identified in PUBMED and the citation metrics for individual articles until 2021 were extracted from Scopus. The primary outcome was the citation count of the article, while the tweet count on Twitter of the article was considered a mediator. The predictors included paper type, the total number of authors, the proportion of authors with a ResearchGate account in the article, funding support, open‐accessed article, and the number of different countries stated in the authors' affiliation. A mediation analysis was conducted to examine the predictors' direct and indirect effects (i.e., via tweet count) on the citation count of the article. Results: A total of 2210 articles were included in this study, of which 223 (10.1%) were review articles. The median (IQR) number of Scopus citations, tweets, countries, and percentage of authors with ResearchGate accounts were 12 (6–21), 2 (0–6), 1 (1–1), and 75% (50%–100%) respectively. In the mediation analysis, tweet count, article type, number of countries, percentage of authors with a ResearchGate account, and journal impact factors in 2014 were positively associated with the Scopus citation count. The effects of article type, open access, and journals' impact factors in 2014 on Scopus citation count were mediated by the tweet count. Conclusion: This study provides empirical support for some strategies researchers may employ to amplify the citation count of their research articles. The methodology of our study can be extended to compare research influence between entities (e.g., across countries or institutes). Clinical Relevance: The citation refers to the research work cited by peers and is one of the indicators for research impact. Higher citations implied the research work is read and used by others, therefore, understanding the associated factors with higher citations is critical. [ABSTRACT FROM AUTHOR]

Suicide notes have been regarded as one of the most informative data sources to understand the reasons why people commit suicide. However, there is a paucity of suicide note studies, leaving researchers with an assumption that this phenomenon remains static over time. This study examines this assumption by comparing the characteristics of note-leavers of the same population at 2 different points in time. The authors investigated the characteristics of 316 note-leavers in 2000 in Hong Kong and compared them to note-leavers in 1992. The proportion of people who left suicide notes increased from 20% to 35% between 1992 and 2000, a change that may be related to changes in suicide methods. Note-leavers in 2000 were characterized as individuals aged 25-59 years old, employed, with no physical illness, no psychiatric illness, and died as a result of charcoal-burning. Present data suggest that reasons for leaving suicide notes may have changed over the 8 years from 1992 to 2000.