Hospital-based generalist social workers’ views of what facilitates or hinders collaboration with specialist palliative care social workers:a grounded theory

Background: In the United States inpatient generalist social workers in discharge planning roles work alongside specialist palliative care social workers to care for patients. As a result two social workers may concurrently be involved in the same patient’s care. Previous studies identifying components of effective collaboration, which impacts patient outcomes, care efficiency, professional job satisfaction, and healthcare costs, were conducted with nurses and physicians. The components of effective collaboration for generalist social workers’ interactions with specialist palliative care social workers are unknown. Aim: To explore inpatient generalist social workers’ views of what facilitates or hinders collaboration with specialist palliative care social workers. Methods: Using a grounded theory approach, qualitative interviews with inpatient generalist social workers (n=14) were systematically analysed to develop a theoretical model of generalist social workers’ collaboration with specialist palliative care social workers. Results: The emerging theoretical model of collaboration consists of: 1) trust, which is comprised of a) ability, b) benevolence, and c) integrity, 2) information sharing, and 3) role negotiation. Effective collaboration occurs when all pieces of the model are present. Collaboration is facilitated when generalists’ perceptions of trust are high, pertinent information is communicated in a time-sensitive manner, and a flexible approach to roles is taken. Conclusion: Trust is increased when generalist social workers’ perceive the specialist palliative care social worker has the necessary skills to identify and address patient needs, manage interactions with the multidisciplinary healthcare team, support the generalists’ roles, and adheres to social work professional values. Opportunities for formal and informal communication boost collaboration, along with regular access to the specialist palliative care social worker. At the organisational level effective collaboration is hindered by a lack of clarity regarding roles. Research about specialist palliative care social workers’ perceptions of what facilitates or hinders collaboration with generalist social workers is needed

What are the views of hospital-based generalist palliative care professionals on what facilitates or hinders collaboration with in-patient specialist palliative care teams?:a systematically constructed narrative synthesis

Background: Hospital-based specialist palliative care services are common, yet existing evidence of inpatient generalist providers’ perceptions of collaborating with hospital-based specialist palliative care teams has never been systematically assessed. Aim: To assess the existing evidence of inpatient generalist palliative care providers’ perceptions of what facilitates or hinders collaboration with hospital-based specialist palliative care teams. Design: Narrative literature synthesis with systematically constructed search. Data sources: PsycINFO, PubMed, Web of Science, Cumulative Index of Nursing and Allied Health Literature and ProQuest Social Services databases were searched up to December 2014. Individual journal, citation and reference searching were also conducted. Papers with the views of generalist inpatient professional caregivers who utilised hospital-based specialist palliative care team services were included in the narrative synthesis. Hawker’s criteria were used to assess the quality of the included studies. Results: Studies included (n = 23) represented a variety of inpatient generalist palliative care professionals’ experiences of collaborating with specialist palliative care. Effective collaboration is experienced by many generalist professionals. Five themes were identified as improving or decreasing effective collaboration: model of care (integrated vs linear), professional onus, expertise and trust, skill building versus deskilling and specialist palliative care operations. Collaboration is fostered when specialist palliative care teams practice proactive communication, role negotiation and shared problem-solving and recognise generalists’ expertise. Conclusion: Fuller integration of specialist palliative care services, timely sharing of information and mutual respect increase generalists’ perceptions of effective collaboration. Further research is needed regarding the experiences of non-physician and non-nursing professionals as their views were either not included or not explicitly reported

Ward social workers’ views of what facilitates or hinders collaboration with specialist palliative care team social workers:a grounded theory

Background Inpatient, generalist social workers in discharge planning roles work alongside specialist palliative care social workers to care for patients, often resulting in two social workers being concurrently involved in the same patient’s care. Previous studies identifying components of effective collaboration, which impacts patient outcomes, care efficiency, professional job satisfaction, and healthcare costs, were conducted with nurses and physicians but not social workers. This study explores ward social workers’ perceptions of what facilitates or hinders collaboration with palliative care social workers. Methods Grounded theory was used to explore the research aim. In-depth qualitative interviews with masters trained ward social workers (n = 14) working in six hospitals located in the Midwest, United States were conducted between February 2014 and January 2015. A theoretical model of ward social workers’ collaboration with palliative care social workers was developed. Results The emerging model of collaboration consists of: 1) trust, which is comprised of a) ability, b) benevolence, and c) integrity, 2) information sharing, and 3) role negotiation. Effective collaboration occurs when all elements of the model are present. Conclusion Collaboration is facilitated when ward social workers’ perceptions of trust are high, pertinent information is communicated in a time-sensitive manner, and a flexible approach to roles is taken. The theoretical model of collaboration can inform organisational policy and social work clinical practice guidelines, and may be of use to other healthcare professionals, as improvements in collaboration among healthcare providers may have a positive impact on patient outcomes

Interprofessional Intervention to Improve Geriatric Consultation Timing on an Acute Medical Service

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147184/1/jgs15582_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147184/2/jgs15582.pd

Exploring ChatGPT’s Clinical Ethics Ability: A pilot study

Background: Artificial Intelligence (AI), particularly public models like ChatGPT, has revolutionized the generation of human-like thought processes and text. Across healthcare, the integration of AI in decision-making processes is increasingly pervasive. However, the application of AI in ethical decision-making remains relatively unexplored. Methods: Ethics consultation notes from a tertiary academic medical center were de-identified. We trained ChatGPT using three separate ‘chats’ with one, two, or five unique notes and asked it to produce an ethical analysis/discussion and recommendations for a test case. We conducted this same series again but gave ChatGPT only the ethical analysis/discussion and recommendation sections from the training notes to learn from. Two independent raters scored ChatGPT’s ethics consultation documentation using the validated Ethics Consult Quality Assessment Tool (ECQAT). Results: When trained with full notes ChatGPT’s ECQAT overall holistic rating score for each ‘chat’ was 2.5 for one note, 1.5 for two, and 2.5 for five. When trained using only the ethical analysis/discussion and recommendation sections, ChatGPT scored 3 for one note, 2 for two, and 1 for five. Conclusion: ChatGPT\u27s variable performance, influenced by training data, highlights its poor baseline ability and the need for targeted training. While initial improvement was observed with example consultations, complexity and scale affected performance adversely. The findings emphasize the importance of human oversight, as ChatGPT alone is unable to match human expertise. ChatGPT does exhibit potential for substantial improvement with better training and further research is needed to successfully make use of this powerful and widely accessible tool

Palliative Care for Patients With Cancer: ASCO Guideline Update

PURPOSE: To provide evidence-based guidance to oncology clinicians, patients, nonprofessional caregivers, and palliative care clinicians to update the 2016 ASCO guideline on the integration of palliative care into standard oncology for all patients diagnosed with cancer. METHODS: ASCO convened an Expert Panel of medical, radiation, hematology-oncology, oncology nursing, palliative care, social work, ethics, advocacy, and psycho-oncology experts. The Panel conducted a literature search, including systematic reviews, meta-analyses, and randomized controlled trials published from 2015-2023. Outcomes of interest included quality of life (QOL), patient satisfaction, physical and psychological symptoms, survival, and caregiver burden. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations. RESULTS: The literature search identified 52 relevant studies to inform the evidence base for this guideline. RECOMMENDATIONS: Evidence-based recommendations address the integration of palliative care in oncology. Oncology clinicians should refer patients with advanced solid tumors and hematologic malignancies to specialized interdisciplinary palliative care teams that provide outpatient and inpatient care beginning early in the course of the disease, alongside active treatment of their cancer. For patients with cancer with unaddressed physical, psychosocial, or spiritual distress, cancer care programs should provide dedicated specialist palliative care services complementing existing or emerging supportive care interventions. Oncology clinicians from across the interdisciplinary cancer care team may refer the caregivers (eg, family, chosen family, and friends) of patients with cancer to palliative care teams for additional support. The Expert Panel suggests early palliative care involvement, especially for patients with uncontrolled symptoms and QOL concerns. Clinicians caring for patients with solid tumors on phase I cancer trials may also refer them to specialist palliative care.Additional information is available at www.asco.org/supportive-care-guidelines

AusTraits: a curated plant trait database for the Australian flora

INTRODUCTION AusTraits is a transformative database, containing measurements on the traits of Australia’s plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 250 distinct sources, describing > 400 plant traits and > 26,000 taxa. To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project’s GitHub repository, http://traitecoevo.github.io/austraits.build/. Further information on the project is available in the associated publication and at the project website austraits.org. Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 CONTRIBUTORS The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University. We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment, Land, Water and Planning, Victoria. AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their “Transformative data collections” (https://doi.org/10.47486/TD044) and “Data Partnerships” (https://doi.org/10.47486/DP720) programs; fellowship grants from Australian Research Council to Falster (FT160100113), Gallagher (DE170100208) and Wright (FT100100910), a grant from Macquarie University to Gallagher. The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS). ACCESSING AND USE OF DATA The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community. A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors: Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 In addition, we encourage users you to cite the original data sources, wherever possible. Note that under the license data may be redistributed, provided the attribution is maintained. The downloads below provide the data in two formats: austraits-3.0.2.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python. austraits-3.0.2.rds: data as compressed R object. Suitable for users of R (see below). Both objects contain all the data and relevant meta-data. AUSTRAITS R PACKAGE For R users, access and manipulation of data is assisted with the austraits R package. The package can both download data and provides examples and functions for running queries. STRUCTURE OF AUSTRAITS The compiled AusTraits database has the following main components: austraits ├── traits ├── sites ├── contexts ├── methods ├── excluded_data ├── taxanomic_updates ├── taxa ├── definitions ├── contributors ├── sources └── build_info These elements include all the data and contextual information submitted with each contributed datasets. A schema and definitions for the database are given in the file/component definitions, available within the download. The file dictionary.html provides the same information in textual format. Full details on each of these components and columns are contained within the definition. Similar information is available at http://traitecoevo.github.io/austraits.build/articles/Trait_definitions.html and http://traitecoevo.github.io/austraits.build/articles/austraits_database_structure.html. CONTRIBUTING We envision AusTraits as an on-going collaborative community resource that: Increases our collective understanding the Australian flora; and Facilitates accumulation and sharing of trait data; Builds a sense of community among contributors and users; and Aspires to fully transparent and reproducible research of the highest standard. As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at traitecoevo/austraits.build. Here are some of the ways you can contribute: Reporting Errors: If you notice a possible error in AusTraits, please post an issue on GitHub. Refining documentation: We welcome additions and edits that make using the existing data or adding new data easier for the community. Contributing new data: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at http://traitecoevo.github.io/austraits.build/articles/contributing_data.html

AusTraits, a curated plant trait database for the Australian flora

International audienceWe introduce the austraits database-a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual-and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge