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This study aimed to investigate the association between smoking behaviours during early pregnancy and the risk and severity of gestational diabetes mellitus (GDM), with a particular focus on smoking status, smoking intensity and secondhand smoke exposure.
Secondary analysis of prospectively collected cohort data.
Multi-centre study conducted in South Korea (Korean Pregnancy Outcome Study) between March 2013 and January 2017.
From 4537 pregnant women initially enrolled, 3457 singleton pregnancies were included after excluding cases with transfer, loss to follow-up, twin pregnancies, miscarriages and pre-existing diabetes mellitus. All participants were women of Korean ethnicity.
Primary outcome was GDM and its subtypes (A1GDM: diet-controlled; A2GDM: insulin-requiring). Secondary outcomes were associations with active smoking (before pregnancy and during early pregnancy), smoking intensity dose–response relationships (pack-years) and secondhand smoke exposure among never-smokers.
Among 3457 participants, 231 women (6.7%) were diagnosed with GDM (198 A1GDM, 33 A2GDM). Active smoking before pregnancy (adjusted OR (aOR) 3.98, 95% CI 1.58 to 9.30) and during early pregnancy (aOR 9.90, 95% CI 2.97 to 29.45) were significantly associated with A2GDM, while no significant association was observed with A1GDM. A clear dose-response relationship was observed, with smoking intensity >4 pack-years markedly increasing A2GDM risk (aOR 20.68, 95% CI 6.75 to 59.39). Detailed pack-year analysis showed 4–6 pack-years (aOR 20.57, 95% CI 5.80 to 65.46) and >6 pack-years (aOR 25.98, 95% CI 3.21 to 146.45). Among never-smokers, secondhand smoke exposure showed a borderline association with overall GDM risk (aOR 1.33, 95% CI 0.98 to 1.81).
Maternal active smoking before and during early pregnancy, as well as higher smoking intensity, was associated with an increased risk of pharmacologically treated GDM (A2GDM). Although secondhand smoke exposure did not reach statistical significance, the trend suggested a potential association with GDM risk among never-smokers. These findings provide important evidence for public health strategies for prenatal care, as smoking cessation and environmental smoke avoidance during prenatal and early antenatal care in women reduce the risk of gestational diabetes.
To determine the clinical characteristics and identify not only the Korean Triage and Acuity Scale levels of older patients with pneumonia in the emergency department but also the factors associated with their survival.
This study employed a retrospective observational design.
This study was conducted at the emergency department of a university hospital in Seoul, South Korea. It utilised medical data from January 1 to December 31, 2023. The study sample comprised 327 patients aged 65 years or older who received a pneumonia diagnosis (International Classification of Diseases: J10–J18). Binary logistic regression analysis was performed to identify independent factors associated with their survival.
Survival was significantly associated with and influenced by sex (specifically, male sex), initial Korean Triage and Acuity Scale level, oxygen supplementation in the emergency department, consciousness level (specifically, painful response), body temperature (> 37.5°C) and a diagnosis of solid or hematologic malignancies.
The findings highlight the need for improved triage protocols, emphasising consciousness level, body temperature and malignancies. Incorporating geriatric-specific age thresholds and oncologic status into Korean Triage and Acuity Scale classifications may enhance risk stratification, timely intervention and resource allocation in emergency department settings.
This study provides insight into triage accuracy for older pneumonia patients, emphasising early recognition of high-risk individuals and strengthening nursing assessment protocols. Improved Korean Triage and Acuity Scale classifications can optimise resource allocation and emergency care strategies, ultimately reducing mortality rates.
The study provides actionable insights for emergency nurses, triage clinicians and policymakers. The findings support the refinement of KTAS protocols to enhance risk stratification and guide resource allocation for older pneumonia patients, ultimately aiming to reduce mortality rates.
Adhered to STROBE guidelines for observational studies.
Although patients did not directly participate, the findings advocate for patient-centred triage improvements, enhancing early identification of high-risk older patients with pneumonia.
Despite the continued burden of opioid overdose in communities in the USA, effective treatments for opioid use disorder (OUD), such as medication for opioid use disorder (MOUD), remain underused. Motivational interviewing techniques and linkage to MOUD via digital health are innovative practices developed to overcome persistent barriers to accessing MOUD treatment. These practices are merged in a comprehensive digital health platform, RecoveryPad (developed by the Center for Progressive Recovery). Our study, ‘Using System Dynamics Modeling to Foster Real-time Connections to Care’ (NIH Award #: 1R61DA057675-01), is a pilot to assess the feasibility and behaviour change potential of RecoveryPad for our target audience of people experiencing OUD.
This study will recruit 40 participants in Connecticut and New York through online platforms, such as social media and digital advertising, and direct access via quick-response (QR) codes distributed by local community partners. Eligibility assessment and enrolment will be conducted virtually. Individuals reporting symptoms indicating moderate to severe OUD who are at least 18 years of age are eligible for the study, excluding those who are currently receiving MOUD, pregnant or incarcerated. Enrolled participants will interact with an automated chatbot, live recovery coaches and, if desired, be referred to a telehealth MOUD provider via the RecoveryPad platform. Participants will have access to the platform for 30 days and will be asked to complete brief surveys to assess MOUD engagement and secondary outcomes at 30 and 90 days. Additionally, system dynamics (SD) models will be developed at the individual level to simulate participant interactions with RecoveryPad, and at the community level to improve understanding of the systems affecting OUD and MOUD access.
This project received approval from the Yale University Human Investigation Committee in 2024 (HIC # 2000034414). All participants will complete an electronic consent form with detailed study information and release of information to obtain data related to MOUD appointment attendance. Findings and conclusions from this pilot will be disseminated via peer-reviewed publication, advisory board meetings and meetings with community partners.
Clinical supervision is claimed to benefit nurses' wellbeing, professional development and practice. However, evaluations highlight implementation challenges, and universal uptake among nurses is uncommon, which limits benefits and the quality of evaluations. This paper reports outcomes of a government policy initiative to implement clinical supervision in Victoria, Australia, with survey data generated through a program logic evaluation.
To explore nurses' perceptions of the implementation of clinical supervision, specifically addressing training adequacy, participation rates, organisational support, cultural growth and relational capacity development.
A cross-sectional survey of nursing supervisees was conducted within a program of research investigating the governmental implementation of clinical supervision.
A survey of 366 participating nurses across four separate organisations addressed the following outcome evaluation questions, arising from program logic objectives: Are nurses currently engaged in clinical supervision? What is the relationship between clinical supervision implementation and: (1) nurses' preparation for clinical supervision, (2) their experience of the organisation valuing clinical supervision and (3) valuing nurses' own wellbeing and (4) nurses' perception of their own growth in relational practice?
The findings affirm the clinical supervision implementation program by showing positive associations for the intended outcomes. Nurses reported: they had sufficient training in clinical supervision; their workplaces were experienced as supportive of clinical supervision and nurturing of the participants; and they had growth in relational ability. Each positive finding was significantly stronger for the sub-sample (65%) of study participants who were currently engaged in clinical supervision compared to those who were not.
The study foregrounds the contribution of program logic, within a multifaceted initiative and including a strong authorising environment, to the implementation of clinical supervision.
Implementation of clinical supervision across services can be enabled by values-congruent strategies, including high-level authorising, stakeholder objective setting, training and coalition of change agents.
This paper addresses the gap between numerous local intervention studies of clinical supervision for nurses and the lack of empirical studies informing system-wide implementation approaches. Our survey investigating implementation outcomes shows that nurses experience of the implementation was aligned to program objectives: participating nurses considered themselves effectively trained for clinical supervision and supported by the organisation, with a positive impact on their own practice. This study can assist organisations in considering large-scale implementation of clinical supervision, with a future focus on levels of uptake and impact on practice.
We have adhered to relevant EQUATOR guidelines for survey method (i.e., the CROSS checklist).
Mental Health Consumer and Carer Advisors within the Office of the Chief Mental Health Nurse, Department of Health and Human Services, Victoria, contributed to the establishment of the research evaluation objectives and related survey items. They contributed perspectives via initial project design meetings and further feedback informing the final version of the program logic.
The aim of this review is to identify and map the evidence available on the factors that influence the implementation of clinical supervision for nurses.
The scoping review was conducted and reported following the JBI methodology for scoping reviews.
Searches were conducted on MEDLINE, PsycINFO, and CINAHL databases on 28 March 2023.
A total of 1398 studies were imported into Covidence for screening. Researchers screened the papers according to the inclusion criteria. Empirical studies in English focusing on the implementation of clinical supervision for nurses were included, without year restrictions. Data from 16 studies were extracted and organized according to the constructs within the Consolidated Framework for Implementation Research (CFIR) domains: intervention characteristics, outer setting, inner setting, characteristics of individuals, and process.
When compared with the CFIR constructs, it was found that the influence of the outer setting on implementation was less explored in the literature. Most of the reviewed data highlighted recurring factors, particularly logistical challenges of nursing work such as shift work and lack of control over work time. Organizational culture and managerial support were also identified as significant factors in the implementation. Another significant challenge in implementation was the variety in clinical supervision's design, purpose, and application, despite sharing the same label, leading to questions about whether studies are implementing the same practice.
Policy documents should clearly define both the design and purpose of clinical supervision, beyond just its conceptual definition. Greater emphasis on equitable implementation of clinical supervision is necessary to prevent perpetuating existing inequalities. We conclude that implementation of such complex interventions is not linear, and the implementation strategies need to align with expected implementation challenges.
The advantage of using the implementation framework lies not only in observing what exists as a form of evidence but also in identifying what is underdeveloped. Healthcare services and policy developers can utilize our review to recognize and address potential challenges in introducing, modifying, scaling up, or sustaining their clinical supervision implementation.
No patient or public contribution.
Although artificial intelligence (AI) has been widely applied to electronic health record (EHR) data in hospital environments, its use in long-term care (LTC) facilities remains unexplored. Limited information technology infrastructure and unique challenges in LTC settings require a comprehensive examination of AI’s potential to enhance care quality and operational efficiency. With the aim of examining the application of AI to EHR data in LTC facilities, this scoping review will identify current AI applications for EHR in LTC, informing future research and potential care improvements in LTC settings.
This review will follow the scoping review methodological guidelines. The protocol of this scoping review has been registered on the Open Science Framework. The inclusion criteria are EHR (participants), AI (concept) and LTC facilities (context), with no date restrictions, but limited to articles published in English. Studies of any design focusing on AI applications for EHR in LTC settings will be considered. A systematic search will be performed on MEDLINE (Ovid), CINAHL (EBSCOhost), the Cochrane Central Register of Controlled Trials (Ovid), the Cochrane Database of Systematic Reviews (Ovid) and SCOPUS (Elsevier) by an information specialist. Two reviewers will independently screen titles and abstracts for inclusion based on predefined criteria. The same process will be repeated for full-text screening. Discrepancies will be resolved through team meetings with the third, fourth and fifth reviewers. All reasons for exclusion at the full-text stage will be documented and reported, with any discrepancies resolved by a review team.
As the data will be collected from existing literature, ethical approval is not required. The findings will be disseminated through conference presentations and publication in a peer-reviewed journal. The results will map current knowledge on AI applications in LTC facilities, thereby providing a foundation for future research aimed at enhancing the implementation and effectiveness of AI technologies in such settings.
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