AMO perspectives on the well-being of neurodivergent human capital

Purpose: Existing management research and management practices frequently overlook the relationship between the above-average human capital of highly functioning neurodivergent employees, their subjective well-being in the workplace and performance outcomes. This paper calls for greater attention to the hidden human capital associated with neurodiversity by mainstreaming implementation of neurodiversity-friendly policies and practices. Design/methodology/approach: Drawing on the ability, motivation and opportunity (AMO) framework, this conceptual paper integrates research on employee neurodiversity and well-being to provide a model of HR-systems level and human capital development policies, systems and practices for neurodivergent minorities in the workplace. Findings: This paper illustrates that workplace neurodiversity, like biodiversity, is a natural phenomenon. For subjective individual psychological and organisational well-being, neurodivergent employees require an empathetic culture and innovative talent management approaches that respect cognitive differences. Practical implications: The case is made for neurodivergent human capital development and policy-makers to promote inclusive employment and decent work in a context of relatively high unemployment for neurodivergent individuals. Originality/value: This paper extends current debates on organisational equality, diversity and inclusion to a consideration of workplace well-being for highly functioning neurodivergent workers. It calls for more equitable and empathetic approaches to investing in employees with neurodevelopmental and cognitive disabilities

AMO perspectives on the well-being of neurodivergent human capital

Existing management research and management practices frequently overlook the relationship between the above-average human capital of highly functioning neurodivergent employees, their subjective well-being in the workplace and performance outcomes. This paper calls for greater attention to the hidden human capital associated with neurodiversity by mainstreaming implementation of neurodiversity-friendly policies and practices. Drawing on the ability, motivation and opportunity (AMO) framework, this conceptual paper integrates research on employee neurodiversity and well-being to provide a model of HR-systems level and human capital development policies, systems and practices for neurodivergent minorities in the workplace. This paper illustrates that workplace neurodiversity, like biodiversity, is a natural phenomenon. For subjective individual psychological and organisational well-being, neurodivergent employees require an empathetic culture and innovative talent management approaches that respect cognitive differences

The health informatics cohort enhancement project (HICE): using routinely collected primary care data to identify people with a lifetime diagnosis of psychotic disorder

Background: We have previously demonstrated that routinely collected primary care data can be used to identify potential participants for trials in depression [1]. Here we demonstrate how patients with psychotic disorders can be identified from primary care records for potential inclusion in a cohort study. We discuss the strengths and limitations of this approach; assess its potential value and report challenges encountered. Methods: We designed an algorithm with which we searched for patients with a lifetime diagnosis of psychotic disorders within the Secure Anonymised Information Linkage (SAIL) database of routinely collected health data. The algorithm was validated against the "gold standard" of a well established operational criteria checklist for psychotic and affective illness (OPCRIT). Case notes of 100 patients from a community mental health team (CMHT) in Swansea were studied of whom 80 had matched GP records. Results: The algorithm had favourable test characteristics, with a very good ability to detect patients with psychotic disorders (sensitivity > 0.7) and an excellent ability not to falsely identify patients with psychotic disorders (specificity > 0.9). Conclusions: With certain limitations our algorithm can be used to search the general practice data and reliably identify patients with psychotic disorders. This may be useful in identifying candidates for potential inclusion in cohort studies

Role of microRNA in muscle regeneration and diseases related to muscle dysfunction in atrophy, cachexia, osteoporosis, and osteoarthritis

MicroRNAs (miRNAs) are a class of small non-coding RNAs that have emerged as potential predictive, prognostic, and therapeutic biomarkers, relevant to many pathophysiological conditions including limb immobilization, osteoarthritis, sarcopenia, and cachexia. Impaired musculoskeletal homeostasis leads to distinct muscle atrophies. Understanding miRNA involvement in the molecular mechanisms underpinning conditions such as muscle wasting may be critical to developing new strategies to improve patient management. MicroRNAs are powerful post-transcriptional regulators of gene expression in muscle and, importantly, are also detectable in the circulation. MicroRNAs are established modulators of muscle satellite stem cell activation, proliferation, and differentiation, however, there have been limited human studies that investigate miRNAs in muscle wasting. This narrative review summarizes the current knowledge as to the role of miRNAs in the skeletal muscle differentiation and atrophy, synthesizing the findings of published data

An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data

Validated methods of identifying childhood maltreatment (CM) in primary and secondary care data are needed. We aimed to create the first externally validated algorithm for identifying maltreatment using routinely collected healthcare data. Comprehensive code lists were created for use within GP and hospital admissions datasets in the SAIL Databank at Swansea University working with safeguarding clinicians and academics. These code lists build on and refine those previously published to include an exhaustive set of codes. Sensitivity, specificity and positive predictive value of previously published lists and the new algorithm were estimated against a clinically assessed cohort of CM cases from a child protection service secondary care-based setting—‘the gold standard’. We conducted sensitivity analyses to examine the utility of wider codes indicating Possible CM. Trends over time from 2004 to 2020 were calculated using Poisson regression modelling. Our algorithm outperformed previously published lists identifying 43–72% of cases in primary care with a specificity ≥ 85%. Sensitivity of algorithms for identifying maltreatment in hospital admissions data was lower identifying between 9 and 28% of cases with high specificity (> 96%). Manual searching of records for those cases identified by the external dataset but not recorded in primary care suggest that this code list is exhaustive. Exploration of missed cases shows that hospital admissions data is often focused on the injury being treated rather than recording the presence of maltreatment. The absence of child protection or social care codes in hospital admissions data poses a limitation for identifying maltreatment in admissions data. Linking across GP and hospital admissions maximises the number of cases of maltreatment that can be accurately identified. Incidence of maltreatment in primary care using these code lists has increased over time. The updated algorithm has improved our ability to detect CM in routinely collected healthcare data. It is important to recognize the limitations of identifying maltreatment in individual healthcare datasets. The inclusion of child protection codes in primary care data makes this an important setting for identifying CM, whereas hospital admissions data is often focused on injuries with CM codes often absent. Implications and utility of algorithms for future research are discussed

Establishment of clinical exercise physiology as a regulated healthcare profession in the UK:a progress report

In 2021, a 'call to action' was published to highlight the need for professional regulation of clinical exercise physiologists to be established within UK healthcare systems to ensure patient safety and align training and regulation with other health professions. This manuscript provides a progress report on the actions that Clinical Exercise Physiology UK (CEP-UK) has undertaken over the past 4 years, during which time clinical exercise physiologists have implemented regulation and gained formal recognition as healthcare professionals in the UK. An overview of the consultation process involved in creating a regulated health profession, notably the development of policies and procedures for both individual registration and institutional master's degree (MSc) accreditation is outlined. Additionally, the process for developing an industry-recognised scope of practice, a university MSc-level curriculum framework, the Academy for Healthcare Science Practitioner standards of proficiency and Continuing Professional Development opportunities is included. We outline the significant activities and milestones undertaken by CEP-UK and provide insight and clarity for other health professionals to understand the training and registration process for a clinical exercise physiologist in the UK. Finally, we include short, medium and long-term objectives for the future advocacy development of this workforce in the UK.</p

Premature Mortality among People with Severe Mental Illness – New Evidence from Linked Primary Care Data

Introduction Studies assessing premature mortality in people with severe mental illness (SMI) are often based in one setting, hospital (secondary care inpatients and/or outpatients) or community (primary care). This may lead to ascertainment bias. Objectives and Approach This study aimed to estimate standardised mortality ratios (SMRs) for all-cause and cause-specific mortality in people with SMI drawn from linked primary and secondary care populations compared to the general population. Standardised mortality ratios (SMRs) were calculated using the indirect method for a United Kingdom population of almost four million between 2004-2013. Results The all-cause SMR was higher in the cohort identified from secondary care hospital admissions (SMR: 2.9; 95% CI: 2.8-3.0) than from primary care (SMR: 2.2; 95% CI: 2.1-2.3) when compared to the general population. The SMR for the combined cohort was 2.6 (95% CI: 2.5-2.6). Solely hospital admission based studies may somewhat over-estimate premature mortality in those with SMI. However, there is no doubt this remains a major health inequality. Cause specific SMRs in the combined cohort were particularly elevated in those with SMI relative to the general population for ill-defined and unknown causes, suicide, and substance abuse, as well as a number of other causes. Conclusion/Implications The ability to combine cohorts electronically from primary and secondary care is more representative of the whole population. Comprehensive characterisation of mortality is important to inform policy and practice and to discriminate settings to allow for proportionate interventions to address this health injustice

Premature mortality among people with severe mental illness — New evidence from linked primary care data

Studies assessing premature mortality in people with severe mental illness (SMI) are usually based in one setting, hospital (secondary care inpatients and/or outpatients) or community (primary care). This may lead to ascertainment bias. This study aimed to estimate standardised mortality ratios (SMRs) for all-cause and cause-specific mortality in people with SMI drawn from linked primary and secondary care populations compared to the general population. SMRs were calculated using the indirect method for a United Kingdom population of almost four million between 2004-2013. The all-cause SMR was higher in the cohort identified from secondary care hospital admissions (SMR: 2.9; 95% CI: 2.8-3.0) than from primary care (SMR: 2.2; 95% CI: 2.1-2.3) when compared to the general population. The SMR for the combined cohort was 2.6 (95% CI: 2.5-2.6). Cause specific SMRs in the combined cohort were particularly elevated in those with SMI relative to the general population for ill-defined and unknown causes, suicide, substance abuse, Parkinson’s disease, accidents, dementia, infections and respiratory disorders (particularly pneumonia), and Alzheimer’s disease. Solely hospital admission based studies, which have dominated the literature hitherto, somewhat over-estimate premature mortality in those with SMI. People with SMI are more likely to die by ill-defined and unknown causes, suicide and other less common and often under-reported causes. Comprehensive characterisation of mortality is important to inform policy and practice and to discriminate settings to allow for proportionate interventions to address this health injustice