Analysis of England's incident and mental health nursing workforce data 2015-2022.

WHAT IS KNOWN ON THE SUBJECT?: Mental health services report adverse incidents in different ways and the relationship between adverse incidents and the workforce is uncertain. In England, there are national datasets recording all incidents and workforce statistics though there is no peer-reviewed evidence examining recent trends. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: Although there has been an overall increase in the number of mental health nurses, more are working in the community and the number of nurses relative to adverse incidents has decreased. There have been service-provision changes but the role of mental health nurses has not significantly changed in this period, and we can therefore assume that their current practice is saturated with risk or increased reporting. To help understand the relationship between nurses and incidents, we need to transform how incidents are recorded in England. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: English mental health services report greater levels of patient-related factors such as self-harm or aggression rather than missed or erroneous care. This makes it difficult to understand if a rise in incident frequency is linked to reporting behaviour, patient risk, unsafe/ineffective care or other reasons and therefore planning workforce deployment to improve care quality is problematic. ABSTRACT: INTRODUCTION: There is a paucity of empirical data examining incidents and mental health nurses and the relationship between the two remains uncertain. AIM: Comparison of English national data for incidents and nursing workforce to examine recent trends. METHOD: Descriptive analysis of two national datasets of incidents and workforce data for England between 2015 and 2022. RESULTS: A 46% increase in incidents was found; the leading causes are self-harm and aggressive behaviour. Despite the rise in adverse incident reporting, a 6% increase in mental health nurses was found, with more nurses in community settings than hospitals. DISCUSSION: Current services are incident reporting at greater concentrations than in previous years. Patient-related behaviour continues to be most prominently reported, rather than possible antecedent health services issues that may contribute to reporting. Whilst staffing has increased, this does not seem to have kept pace with the implied workload evident in the increase in incident reports. IMPLICATIONS FOR PRACTICE: Greater emphasis should be placed on health service behaviour in reporting mechanisms. Self-harm and aggression should continue to be considered adverse outcomes, but causal health service factors, such as missed care, should be present in pooled reporting to help reduce the occurrence of adverse outcomes.

Could artificial intelligence write mental health nursing care plans?

WHAT IS KNOWN ON THE SUBJECT?: Artificial intelligence (AI) is freely available, responds to very basic text input (such as a question) and can now create a wide range of outputs, communicating in many languages or art forms. AI platforms like OpenAI's ChatGPT can now create passages of text that could be used to create plans of care for people with mental health needs. As such, AI output can be difficult to distinguish from human-output, and there is a risk that its use could go unnoticed. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: Whilst it is known that AI can produce text or pass pre-registration health-profession exams, it is not known if AI can produce meaningful results for care delivery. We asked ChatGPT basic questions about a fictitious person who presents with self-harm and then evaluated the quality of the output. We found that the output could look reasonable to laypersons but there were significant errors and ethical issues. There are potential harms to people in care if AI is used without an expert correcting or removing these errors. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: We suggest that there is a risk that AI use could cause harm if it was used in direct care delivery. There is a lack of policy and research to safeguard people receiving care - and this needs to be in place before AI should be used in this way. Key aspects of the role of a mental health nurse are relational and AI use may diminish mental health nurses' ability to provide safe care in its current form. Many aspects of mental health recovery are linked to relationships and social engagement, however AI is not able to provide this and may push the people who are in most need of help further away from services that assist recovery. ABSTRACT: Background Artificial intelligence (AI) is being increasingly used and discussed in care contexts. ChatGPT has gained significant attention in popular and scientific literature although how ChatGPT can be used in care-delivery is not yet known. Aims To use artificial intelligence (ChatGPT) to create a mental health nursing care plan and evaluate the quality of the output against the authors' clinical experience and existing guidance. Materials & Methods Basic text commands were input into ChatGPT about a fictitious person called 'Emily' who presents with self-injurious behaviour. The output from ChatGPT was then evaluated against the authors' clinical experience and current (national) care guidance. Results ChatGPT was able to provide a care plan that incorporated some principles of dialectical behaviour therapy, but the output had significant errors and limitations and thus there is a reasonable likelihood of harm if used in this way. Discussion AI use is increasing in direct-care contexts through the use of chatbots or other means. However, AI can inhibit clinician to care-recipient engagement, 'recycle' existing stigma, and introduce error, which may thus diminish the ability for care to uphold personhood and therefore lead to significant avoidable harms. Conclusion Use of AI in this context should be avoided until a point where policy and guidance can safeguard the wellbeing of care recipients and the sophistication of AI output has increased. Given ChatGPT's ability to provide superficially reasonable outputs there is a risk that errors may go unnoticed and thus increase the likelihood of patient harms. Further research evaluating AI output is needed to consider how AI may be used safely in care delivery.

Health and Safety Reps in COVID-19-Representation Unleashed?

The paper explores the role of UK union health and safety representatives and changes to representative structures governing workplace and organisational Occupational Health and Safety (OHS) during COVID-19. It draws upon a survey of 648 UK Trade Union Congress (TUC) Health and Safety (H&S) representatives, as well as case studies of 12 organisations in eight key sectors. The survey indicates expanded union H&S representation, but only half of the respondents reported H&S committees in their organisations. Where formal representative mechanisms existed, they provided the basis for more informal day-to-day engagement between management and the union. However, the present study suggests that the legacy of deregulation and the absence of organisational infrastructures meant that the autonomous collective representation of workers' interests over OHS, independent of structures, was crucial to risk prevention. While joint regulation and engagement over OHS was possible in some workplaces, OHS in the pandemic has been contested. Contestation challenges pre-COVID-19 scholarship suggestingthat H&S representatives had been captured by management in the context of unitarist practice. The tension between union power and the wider legal infrastructure remains salient.

Older Migrants and Overcoming Employment Barriers: Does Community Activism Provide the Answer?

As populations age and pension ages rise, there is a growing interest in the ability of workers to extend working life. In response to a call for a more robust dialogue on the heterogeneity of the older workforce, this article explores the interplay between different employment barriers faced by one group facing significant employment barriers: older migrants. Older Chinese migrants in the United Kingdom face multiple barriers to work resulting from age, ethnicity and the intersectionality of such barriers which creates a unique set of barriers to continued work. Community activism can play an important role in supporting older constituents, particularly in matching the skills which they have to offer with the needs within and beyond the migrant community. In this study, we use Participatory Action Research to explore with older Chinese migrants the barriers they face in the job market and how community activism can empower them in maintaining employment. As government seeks to raise real retirement ages, more research is needed on its implications for vulnerable groups of older people including migrants.

Job Satisfaction, Retirement Attitude and Intended Retirement Age: A Conditional Process Analysis across Workers' Level of Household Income.

In the contemporary workplace, insight into retirement behaviors is of crucial importance. Previous empirical evidence has found mixed results regarding the relationship between work attitudes, such as job satisfaction, and retirement behaviors, suggesting that further scholarly examination incorporating moderating and mediating variables into retirement models is needed. Drawing on comparative models of attitude to retirement, we hypothesized a direct relationship between job satisfaction and intended retirement age for workers with a high household income and an indirect relationship between job satisfaction and intended retirement age, via retirement attitude, for workers with a low or mean household income. We collected data from a sample of 590 United Kingdom workers aged 50+. Using conditional process analysis, we found that the underlying mechanisms in our research model differ according to socio-economic status. We found no direct effect between job satisfaction and intended retirement age. However, an indirect effect was observed between job satisfaction and intended retirement age, via retirement attitude, for both low- and mean-household income individuals. Specifically, the relationship between job satisfaction and retirement attitude differed according to socio-economic group: for high-household income older workers, there was no relationship between job satisfaction and retirement attitude. However, for low- and mean-household income older workers, we observed a negative relationship between job satisfaction and retirement attitude. Otherwise stated, increases in job satisfaction for mean and low household income workers are likely to make the prospect of retirement less attractive. Therefore, we argue that utmost care must be taken around the conditions under which lower income employees will continue their work when getting older in order to protect their sustainable employability.

Identification of anti-tumour biologics using primary tumour models, 3-D phenotypic screening and image-based multi-parametric profiling.

BACKGROUND: Monolayer cultures of immortalised cell lines are a popular screening tool for novel anti-cancer therapeutics, but these methods can be a poor surrogate for disease states, and there is a need for drug screening platforms which are more predictive of clinical outcome. In this study, we describe a phenotypic antibody screen using three-dimensional cultures of primary cells, and image-based multi-parametric profiling in PC-3 cells, to identify anti-cancer biologics against new therapeutic targets. METHODS: ScFv Antibodies and designed ankyrin repeat proteins (DARPins) were isolated using phage display selections against primary non-small cell lung carcinoma cells. The selected molecules were screened for anti-proliferative and pro-apoptotic activity against primary cells grown in three-dimensional culture, and in an ultra-high content screen on a 3-D cultured cell line using multi-parametric profiling to detect treatment-induced phenotypic changes. The targets of molecules of interest were identified using a cell-surface membrane protein array. An anti-CUB domain containing protein 1 (CDCP1) antibody was tested for tumour growth inhibition in a patient-derived xenograft model, generated from a stage-IV non-small cell lung carcinoma, with and without cisplatin. RESULTS: Two primary non-small cell lung carcinoma cell models were established for antibody isolation and primary screening in anti-proliferative and apoptosis assays. These assays identified multiple antibodies demonstrating activity in specific culture formats. A subset of the DARPins was profiled in an ultra-high content multi-parametric screen, where 300 morphological features were measured per sample. Machine learning was used to select features to classify treatment responses, then antibodies were characterised based on the phenotypes that they induced. This method co-classified several DARPins that targeted CDCP1 into two sets with different phenotypes. Finally, an anti-CDCP1 antibody significantly enhanced the efficacy of cisplatin in a patient-derived NSCLC xenograft model. CONCLUSIONS: Phenotypic profiling using complex 3-D cell cultures steers hit selection towards more relevant in vivo phenotypes, and may shed light on subtle mechanistic variations in drug candidates, enabling data-driven decisions for oncology target validation. CDCP1 was identified as a potential target for cisplatin combination therapy.

Combining phenotypic and proteomic approaches to identify membrane targets in a 'triple negative' breast cancer cell type.

BACKGROUND: The continued discovery of therapeutic antibodies, which address unmet medical needs, requires the continued discovery of tractable antibody targets. Multiple protein-level target discovery approaches are available and these can be used in combination to extensively survey relevant cell membranomes. In this study, the MDA-MB-231 cell line was selected for membranome survey as it is a 'triple negative' breast cancer cell line, which represents a cancer subtype that is aggressive and has few treatment options. METHODS: The MDA-MB-231 breast carcinoma cell line was used to explore three membranome target discovery approaches, which were used in parallel to cross-validate the significance of identified antigens. A proteomic approach, which used membrane protein enrichment followed by protein identification by mass spectrometry, was used alongside two phenotypic antibody screening approaches. The first phenotypic screening approach was based on hybridoma technology and the second was based on phage display technology. Antibodies isolated by the phenotypic approaches were tested for cell specificity as well as internalisation and the targets identified were compared to each other as well as those identified by the proteomic approach. An anti-CD73 antibody derived from the phage display-based phenotypic approach was tested for binding to other 'triple negative' breast cancer cell lines and tested for tumour growth inhibitory activity in a MDA-MB-231 xenograft model. RESULTS: All of the approaches identified multiple cell surface markers, including integrins, CD44, EGFR, CD71, galectin-3, CD73 and BCAM, some of which had been previously confirmed as being tractable to antibody therapy. In total, 40 cell surface markers were identified for further study. In addition to cell surface marker identification, the phenotypic antibody screening approaches provided reagent antibodies for target validation studies. This is illustrated using the anti-CD73 antibody, which bound other 'triple negative' breast cancer cell lines and produced significant tumour growth inhibitory activity in a MDA-MB-231 xenograft model. CONCLUSIONS: This study has demonstrated that multiple methods are required to successfully analyse the membranome of a desired cell type. It has also successfully demonstrated that phenotypic antibody screening provides a mechanism for rapidly discovering and evaluating antibody tractable targets, which can significantly accelerate the therapeutic discovery process.