Unravelling the mental health productivity puzzle : where do we start?

In this editorial we define 'productivity' and 'efficiency' in a mental health service context, outlining the key challenges to measuring these in practice. We attempt to bring clarity of thought to this often debated, but rarely researched area

Modelling out-of-area admissions

Out-of-area (OOA) placements occur when patients cannot be admitted to local facilities, which can be extremely stressful for patients and families. Thus, the Department of Health aims to eliminate the need for OOA admissions. Using data from a UK mental health trust we developed a ‘virtual mental health ward’ to evaluate the potential impact of referral rates and length of stay (LOS) on OOA rates. The results indicated OOA rates were equally sensitive to LOS and referral rate. This suggests that investment in community services that reduce both LOS and referral rates are required to meaningfully reduce OOA admission rates

Defining Professionalism for Mental Health Services : A Rapid Systematic Review

Background: Efforts have been made to define professionalism across the professions, yet little attention has been paid to the concept in mental health services, where patients’ needs differ to that in other healthcare specialties. Aims: To derive a definition of professionalism for mental health services using the existing literature. Method: A rapid, systematic review was conducted to identify empirical and non-empirical records that described professionalism in a mental health service context from 2006 to 2017. Studies were synthesised narratively using thematic analysis. Results: Seventy records were included in the review. Professionalism was described on two levels; at a societal level, a dynamic social contract between professions and society, and; at an individual level, having intrapersonal, interpersonal, and working professionalism. Utilising emerging themes, an operationalised definition of professionalism, suitable for a mental health service context was derived. Conclusions: Within mental health services, emphasis is placed on the interpersonal aspects of practice such as communication skills, maintaining boundaries and humanity. Themes relating to the vulnerability of patients and the challenge of supporting autonomy and choice whilst maintaining safety and acting in a client’s best interest are also evident. ‘Practical wisdom’ and a flexible approach to working are needed to manage these challenging situations

'Personalised evidence' for personalised healthcare: integration of a clinical librarian into mental health services – a feasibility study

Aims and method To evaluate the feasibility of integrating a clinical librarian (CL) within four mental health teams. A CL was attached to three clinical teams and the Trustwide Psychology Research and Clinical Governance Structure for 12 months. Requests for evidence syntheses were recorded. The perceived impact of individual evidence summaries on staff activities was evaluated using a brief online questionnaire. Results Overall, 82 requests for evidence summaries were received: 50% related to evidence for individual patient care, 23% to generic clinical issues and 27% were on management/corporate topics. In the questionnaires 105 participants indicated that the most common impact on their practice was advice given to colleagues (51 respondents), closely followed by the evidence summaries stimulating new ideas for patient care or treatment (50 respondents). Clinical implications The integration of a CL into clinical and corporate teams is feasible and perceived as having an impact on staff activities. A CL may be able to collate ‘personalised evidence’ which may enhance individualised healthcare. In some cases the usual concept of a hierarchy of evidence may not easily apply, with case reports providing guidance which may be more applicable than population-based studies

Predicting fitness to practise events in international medical graduates who registered as UK doctors via the Professional and Linguistic Assessments Board (PLAB) system: a national cohort study

Background International medical graduates working in the UK are more likely to be censured in relation to fitness to practise compared to home graduates. Performance on the General Medical Council’s (GMC’s) Professional and Linguistic Assessments Board (PLAB) tests and English fluency have previously been shown to predict later educational performance in this group of doctors. It is unknown whether the PLAB system is also a valid predictor of unprofessional behaviour and malpractice. The findings would have implications for regulatory policy. Methods This was an observational study linking data relating to fitness to practise events (referral or censure), PLAB performance, demographic variables and English language competence, as evaluated via the International English Language Test System (IELTS). Data from 27,330 international medical graduates registered with the GMC were analysed, including 210 doctors who had been sanctioned in relation to at least one fitness to practise issue. The main outcome was risk of eventual censure (including a warning). Results The significant univariable educational predictors of eventual censure (versus no censures or referrals) were lower PLAB part 1 (hazard ratio [HR], 0.99; 95% confidence interval, 0.98 to 1.00) and part 2 scores (HR, 0.94; 0.91 to 0.97) at first sitting, multiple attempts at both parts of the PLAB, lower IELTS reading (HR, 0.79; 0.65 to 0.94) and listening scores (HR, 0.76; 0.62 to 0.93) and higher IELTS speaking scores (HR, 1.28; 1.04 to 1.57). Multiple resits at either part of the PLAB and higher IELTS speaking score (HR, 1.49; 1.20 to 1.84) were also independent predictors of censure. We estimated that the proposed limit of four attempts at both parts of the PLAB would reduce the risk in this entire group by only approximately two censures per 5 years in this group of doctors. Conclusions Making the PLAB, or any replacement assessment, more stringent and raising the required standards of English reading and listening may result in fewer fitness to practice events in international medical graduates. However, the number of PLAB resits permitted would have to be further capped to meaningfully impact the risk of sanctions in this group of doctor

Mental health trajectories in university students across the COVID-19 pandemic: findings from the Student Wellbeing at Northern England Universities prospective cohort study

IntroductionPsychological wellbeing in university students is receiving increased focus. However, to date, few longitudinal studies in this population have been conducted. As such, in 2019, we established the Student Wellbeing At Northern England Universities (SWANS) cohort at the University of York, United Kingdom aiming to measure student mental health and wellbeing every six months. Furthermore, the study period included the COVID-19 pandemic, giving an opportunity to track student wellbeing over time, including over the pandemic.MethodsEligible participants were invited to participate via email. Data were collected, using Qualtrics, from September 2019 to April 2021, across five waves (W1 to W5). In total, n = 4,622 students participated in at least one wave of the survey. Data collection included sociodemographic, educational, personality measures, and mental health and wellbeing. Latent profile analyses were performed, exploring trajectories of student wellbeing over the study period for those who had completed at least three of the five waves of the survey (n = 765), as measured by the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS).ResultsFive latent profile trajectories of student wellbeing were identified. Of these, the two latent classes with initially higher wellbeing scores had broadly stable wellbeing across time (total n = 505, 66%). Two classes had lower initial scores, which lowered further across time (total n = 227, 30%). Additionally, a fifth class of students was identified who improved substantially over the study period, from a mean WEMWBS of 30.4 at W1, to 49.4 at W5 (n = 33, 4%). Risk factors for having less favourable wellbeing trajectories generally included identifying as LGBT+, self-declaring a disability, or previously being diagnosed with a mental health condition.ConclusionOur findings suggest a mixed picture of the effect of the COVID-19 pandemic on student wellbeing, with a majority showing broadly consistent levels of wellbeing across time, a smaller but still substantial group showing a worsening of wellbeing, and a small group that showed a very marked improvement in wellbeing. Those from groups traditionally underrepresented in higher education were most at risk of poorer wellbeing. This raises questions as to whether future support for wellbeing should target specific student subpopulations

Can achievement at medical admission tests predict future performance in postgraduate clinical assessments? A UK-based national cohort study.

OBJECTIVE: To determine whether scores on two undergraduate admissions tests (BioMedical Admissions Test (BMAT) and University Clinical Aptitude Test (UCAT)) predict performance on the postgraduate Membership of the Royal Colleges of Physicians (MRCP) examination, including the clinical examination Practical Assessment of Clinical Examination Skills (PACES). DESIGN: National cohort study. SETTING: Doctors who graduated medical school between 2006 and 2018. PARTICIPANTS: 3045 doctors who had sat BMAT, UCAT and the MRCP. PRIMARY OUTCOME MEASURES: Passing each section of the MRCP at the first attempt, including the clinical assessment PACES. RESULTS: Several BMAT and UCAT subtest scores displayed incremental predictive validity for performance on the first two (written) parts of the MRCP. Only aptitude and skills on BMAT (OR 1.34, 1.08 to 1.67, p=0.01) and verbal reasoning on UCAT (OR 1.34, 1.04 to 1.71, p=0.02) incrementally predicted passing PACES at the first attempt. CONCLUSIONS: Our results imply that the abilities assessed by aptitude and skills and verbal reasoning may be the most important cognitive attributes, of those routinely assessed at selection, for predicting future clinical performance. Selectors may wish to consider placing particular weight on scales assessing these attributes if they wish to select applicants likely to become more competent clinicians. These results are potentially relevant in an international context too, since many admission tests used globally, such as the Medical College Admission Test, assess similar abilities

Predicting persistent depressive symptoms in older adults : a machine learning approach to personalised mental healthcare

Background Depression causes significant physical and psychosocial morbidity. Predicting persistence of depressive symptoms could permit targeted prevention, and lessen the burden of depression. Machine learning is a rapidly expanding field, and such approaches offer powerful predictive abilities. We investigated the utility of a machine learning approach to predict the persistence of depressive symptoms in older adults. Method Baseline demographic and psychometric data from 284 patients were used to predict the likelihood of older adults having persistent depressive symptoms after 12 months, using a machine learning approach (‘extreme gradient boosting’). Predictive performance was compared to a conventional statistical approach (logistic regression). Data were drawn from the ‘treatment-as-usual’ arm of the CASPER (CollAborative care and active surveillance for Screen-Positive EldeRs with subthreshold depression) trial. Results Predictive performance was superior using machine learning compared to logistic regression (mean AUC 0.72 vs. 0.67, p < 0.0001). Using machine learning, an average of 89% of those predicted to have PHQ-9 scores above threshold at 12 months, actually did, compared to 78% using logistic regression. However, mean negative predictive values were somewhat lower for the machine learning approach (45% vs. 35%). Limitations A relatively small sample size potentially limited the predictive power of the algorithm. In addition, PHQ-9 scores were used as an indicator of persistent depressive symptoms, and whilst well validated, a clinical interview would have been preferable. Conclusions : Overall, our findings support the potential application of machine learning in personalised mental healthcare. Keywords DepressionMachine learningOld age psychiatr