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Understanding healthcare professionals’ responses to patient complaints in secondary and tertiary care in the UK: a systematic review and behavioural analysis:[preprint]
No full textBackground.The path of a complaint and patient satisfaction with complaint resolution is often dependent on healthcare professionals’ (HCPs) first response. It is therefore important to understand the influences shaping HCP behaviour. This systematic review aimed to (1) identify the key actors, behaviours and factors influencing HCPs’ responses to complaints, and (2) apply behavioural science frameworks to classify these influences and provide recommendations for more effective complaints management.Methods.A systematic literature review of UK published and unpublished (“grey literature”) studies was conducted (PROSPERO registration: CRD42022301980). Five electronic databases (Scopus, Medline/Ovid, Embase, CINAHL, HMIC) were searched up to September 2021. Eligibility criteria included: studies reporting primary data, conducted in secondary and tertiary care, written in English and published between 2001–2021 (studies from primary care, mental health, forensic, paediatric, or dental care services were excluded). Extracted data included: participant quotations from qualitative studies, results from questionnaire and survey studies, case studies reported in commentaries, and descriptions and summaries of results from reports. Data were synthesised narratively using inductive thematic analysis, followed by deductive mapping to the Theoretical Domains Framework (TDF).Results.22 articles and 3 reports meeting the inclusion criteria were included. A total of 8 actors, 22 behaviours and 24 influences on behaviour were found. Key factors influencing effective management of complaints included HCPs’ beliefs about the value of complaints, knowledge of procedures and available time and resources, and organisational culture and leadership. Defensive practices and high stress levels among HCPs were linked to lack of managerial support, role conflict and a blaming culture within the organisation. Themes mapped predominantly onto the TDF domains of social influences (categorised both as barrier and enabler), beliefs about consequences (barrier) and social/professional role and identity (barrier). Recommendations were generated using the BCW approach.Conclusions.Through the application of behavioural science, we identified a wide range of individual, social/organisational and environmental influences on complaints management in secondary and tertiary care. Our behavioural analysis informed recommendations for intervention content, with particular emphasis on reframing and building on the positive aspects of complaints as an underutilised source of feedback at an individual and organisational level
Delegation of insulin administration to non‐registered healthcare workers in community nursing teams: a qualitative study
Get PDFAimsTo explore stakeholder perspectives on the benefits and/or disadvantages of the delegation of insulin injections to healthcare support workers in community nursing services.DesignQualitative case study.MethodsInterviews with stakeholders purposively sampled from three case sites in England. Data collection took place between October 2020 and July 2021. A reflexive thematic approach to analysis was adopted.ResultsA total of 34 interviews were completed: patients and relatives (n = 7), healthcare support workers (n = 8), registered nurses (n = 10) and senior managers/clinicians (n = 9). Analysis resulted in three themes: (i) Acceptance and confidence, (ii) benefits and (iii) concerns and coping strategies. Delegation was accepted by stakeholders on condition that appropriate training, supervision and governance was in place. Continuing contact between patients and registered nurses, and regular contact between registered nurses and healthcare support workers was deemed essential for clinical safety. Services were reliant on the contribution of healthcare support workers providing insulin injections, particularly during the COVID-19 pandemic. Benefits for service and registered nurses included: flexible team working, increased service capacity and care continuity. Job satisfaction and career development was reported for healthcare support workers. Patients benefit from timely administration, and enhanced relationships with the nursing team. Concerns raised by all stakeholders included potential missed care, remuneration and task shifting.ConclusionDelegation of insulin injections is acceptable to stakeholders and has many benefits when managed effectively.ImpactDemand for community nursing is increasing. Findings of this study suggest that delegation of insulin administration contributes to improving service capacity. Findings highlight the essential role played by key factors such as appropriate training, competency assessment and teamwork, in developing confidence in delegation among stakeholders. Understanding and supporting these factors can help ensure that practice develops in an acceptable, safe and beneficial way, and informs future development of delegation practice in community settings.Patient or Public ContributionA service user group was consulted during the design phase prior to grant application and provided comments on draft findings. Two people with diabetes were members of the project advisory group and contributed to the study design, development of interview questions, monitoring study progress and provided feedback on study findings
Delegation of insulin administration to non‐registered healthcare workers in community nursing teams: a qualitative study
Get PDFAimsTo explore stakeholder perspectives on the benefits and/or disadvantages of the delegation of insulin injections to healthcare support workers in community nursing services.DesignQualitative case study.MethodsInterviews with stakeholders purposively sampled from three case sites in England. Data collection took place between October 2020 and July 2021. A reflexive thematic approach to analysis was adopted.ResultsA total of 34 interviews were completed: patients and relatives (n = 7), healthcare support workers (n = 8), registered nurses (n = 10) and senior managers/clinicians (n = 9). Analysis resulted in three themes: (i) Acceptance and confidence, (ii) benefits and (iii) concerns and coping strategies. Delegation was accepted by stakeholders on condition that appropriate training, supervision and governance was in place. Continuing contact between patients and registered nurses, and regular contact between registered nurses and healthcare support workers was deemed essential for clinical safety. Services were reliant on the contribution of healthcare support workers providing insulin injections, particularly during the COVID-19 pandemic. Benefits for service and registered nurses included: flexible team working, increased service capacity and care continuity. Job satisfaction and career development was reported for healthcare support workers. Patients benefit from timely administration, and enhanced relationships with the nursing team. Concerns raised by all stakeholders included potential missed care, remuneration and task shifting.ConclusionDelegation of insulin injections is acceptable to stakeholders and has many benefits when managed effectively.ImpactDemand for community nursing is increasing. Findings of this study suggest that delegation of insulin administration contributes to improving service capacity. Findings highlight the essential role played by key factors such as appropriate training, competency assessment and teamwork, in developing confidence in delegation among stakeholders. Understanding and supporting these factors can help ensure that practice develops in an acceptable, safe and beneficial way, and informs future development of delegation practice in community settings.Patient or Public ContributionA service user group was consulted during the design phase prior to grant application and provided comments on draft findings. Two people with diabetes were members of the project advisory group and contributed to the study design, development of interview questions, monitoring study progress and provided feedback on study findings
Comparison of deep learning classification models for facial image age estimation in digital forensic investigations
Get PDFThere has been a significant rise in digital forensic investigations containing Indecent Images of Children (IIoC), and one of the major challenges faced by investigators is the time-consuming task of manually investigating images for illicit content. In the UK, law enforcement maintains and uses a standard national repository of IIoC, known as CAID (Child Abuse Image Database), to identify known illegal images by matching their image hashes and metadata. The CAID plays a significant role in making IIoC investigations faster and more effective. However, all images that are not matched through using CAID require manual analysis. Every image has to be viewed and verified as IIoC by investigators. The victim age estimation in the images (i.e., determining whether they are juvenile or adult as this would change the course of the investigation) is a crucial part of this verification process and takes time due to a large number of images to inspect, therefore impacting the speed of the investigation, and consequently victims. This is a time-consuming and challenging task for human investigators. Previous work has demonstrated that deep learning has the capability to estimate age with high accuracy in images. This reduces the number of images that will need to be manually processed, thereby finishing the investigation faster. However, in terms of practical implementation in IIoC investigations, there is an absence of a comparative study using the same datasets to establish the most appropriate deep learning model and classification approach to use. This is important as different models have different capabilities and previous works utilise various binary, multi-class, and regression approaches. It is not yet known which is the most accurate for use in digital forensic investigations. In this paper, we construct an extensive dataset before experimenting with four pre-trained deep learning models: VGG16, ResNet50, Xception, and InceptionV3. We have identified that binary classification works best for the identification of images as a child or adult, with the ResNet50 obtaining the best results in terms of accuracy (91.70%) on unseen images.</p
Lifting the veil of a managerial illusion through the contribution of the humanly integrated operational and functional information system:saying what we do and doing what we say
No full textOrganization and information are inseparable in management science. However, the lack of clarity in the hypotheses used sometimes leads to vagueness about the nature of the link between them, as well as to biased representations for decision-making. The notion of a humanly integrated and stimulating Operational and Functional Information System offers a fruitful perspective to avoid giving in to the ease of an overly normative managerial discourse.</p
How Industry 4.0 and Sustainable Development Goals can enhance lean practices
No full textLean manufacturing and management systems have become a new paradigm for excellence in value generation and delivery for the most competitive companies in the world. With the effect of digital transformation, organizations prefer to apply these approaches in order to create value by providing quality products and services to the end customer, while reducing production and environmental waste. In this context, the lean management approach requires the direct support of both sustainability and Industry 4.0 concepts in order to develop the principles and tools. On the other hand, UNDP's sustainable development goals have a very important place when it is aimed to support sustainable industrialization, strengthen innovation, and ensure operational excellence. This research aims to find answers to “How can Industry 4.0 and sustainable development goals enhance lean practices?” by performing content analysis in various databases in the existing literature.<br/
‘Prepare for unforeseen consequences’: narrative game mechanics as affective mechanics in two interactive fictions and two video games
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