Multi-Tracking Sensor Architectures for Reconstructing Autonomous Vehicle Crashes: An Exploratory Study.

With the continuous development of new sensor features and tracking algorithms for object tracking, researchers have opportunities to experiment using different combinations. However, there is no standard or agreed method for selecting an appropriate architecture for autonomous vehicle (AV) crash reconstruction using multi-sensor-based sensor fusion. This study proposes a novel simulation method for tracking performance evaluation (SMTPE) to solve this problem. The SMTPE helps select the best tracking architecture for AV crash reconstruction. This study reveals that a radar-camera-based centralized tracking architecture of multi-sensor fusion performed the best among three different architectures tested with varying sensor setups, sampling rates, and vehicle crash scenarios. We provide a brief guideline for the best practices in selecting appropriate sensor fusion and tracking architecture arrangements, which can be helpful for future vehicle crash reconstruction and other AV improvement research.

Centrality statistics of symptom networks of schizophrenia: a systematic review.

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

Understanding suicidality in Pacific adolescents in New Zealand using network analysis.

INTRODUCTION: Pacific adolescents in New Zealand (NZ) are three to four times more likely than NZ European adolescents to report suicide attempts and have higher rates of suicidal plans. Suicidal thoughts, plans, and attempts, termed suicidality in this study, result from a complex dynamic interplay of factors, which emerging methodologies like network analysis aim to capture. METHODS: This study used cross-sectional network analysis to model the relationships between suicidality, self-harm, and individual depression symptoms, whilst conditioning on a multi-dimensional set of variables relevant to suicidality. A series of network models were fitted to data from a community sample of New Zealand-born Pacific adolescents (n = 550; 51% male; Mean age (SD) = 17 (0.35)). RESULTS: Self-harm and the depression symptom measuring pessimism had the strongest associations with suicidality, followed by symptoms related to having a negative self-image about looks and sadness. Nonsymptom risk factors for self-harm and suicidality differed markedly. CONCLUSIONS: Depression symptoms varied widely in terms of their contribution to suicidality, highlighting the valuable information gained from analysing depression at the symptom-item level. Reducing the sources of pessimism and building self-esteem presented as potential targets for alleviating suicidality amongst Pacific adolescents in New Zealand. Suicide prevention strategies need to include risk factors for self-harm.

Validation of the Music Therapy Assessment Tool for Awareness in Disorders of Consciousness With the Coma Recovery Scale-Revised.

OBJECTIVE: To examine (1) the concurrent validity of the Music Therapy Assessment Tool for Awareness in Disorders of Consciousness (MATADOC) with the criterion standard Coma Recovery Scale-Revised (CRS-R) for outcomes of awareness in patients with prolonged disorders of consciousness (PDoC), (2) the relationship between MATADOC items and CRS-R function subscales in similar domains, and (3) determine if items/function subscales measure different constructs. DESIGN: A prospective multicentric blinded study with repeated concurrent measures. SETTING: Three inpatient rehabilitation units. PARTICIPANTS: Convenience sample of 74 adults with PDoC (N=74). MAIN OUTCOME MEASURES: The MATADOC protocol elicits behavioral responsiveness using live music in 5 tasks. A total score ranges 0-10 scoring behaviors across 14-items. The CRS-R uses a language-based protocol and scores observed responses ranging from 0-23 in 6 function subscales. Both measures were delivered at 4 concurrent time points over 2 weeks. RESULTS: Fair (κ=0.238, P=.006) ranging to moderate (κ=0.419, P<.001) significant agreement was found between CRS-R and MATADOC diagnostic outcomes. Fair-borderline moderate significant agreement was found for overall diagnostic outcomes across all diagnostic categories (κ=0.397, P=.001). There was moderate significant agreement between measures for motor scores (0.551≤κ≤0.571, P<.001) and visual outcomes (0.192≤κ≤0.415, .001≤P<.005) but no agreement for item/function subscale outcomes assessing auditory responsiveness. Exploratory factor analysis of all items showed 2 factors, suggesting that MATADOC and CRS-R measure the same underlying latent variable (awareness) in different ways and could complement each other for diagnosis and intervention purposes. This was supported by scale analysis, which showed increased reliability when the 2 scales are used together rather than separately. CONCLUSIONS: Unlike the CRS-R, the music-based MATADOC scores auditory localization for complexity of response and categorizes these behaviors as conscious rather than reflexive. The MATADOC may supplement the CRS-R, having a particular role in interdisciplinary programming for providing a more robust assessment of auditory responsiveness because of using nonverbal musical stimuli.

Machine learning and network analysis for diagnosis and prediction in disorders of consciousness.

BACKGROUND: Prolonged Disorders of Consciousness (PDOC) resulting from severe acquired brain injury can lead to complex disabilities that make diagnosis challenging. The role of machine learning (ML) in diagnosing PDOC states and identifying intervention strategies is relatively under-explored, having focused on predicting mortality and poor outcome. This study aims to: (a) apply ML techniques to predict PDOC diagnostic states from variables obtained from two non-invasive neurobehavior assessment tools; and (b) apply network analysis for guiding possible intervention strategies. METHODS: The Coma Recovery Scale-Revised (CRS-R) is a well-established tool for assessing patients with PDOC. More recently, music has been found to be a useful medium for assessment of coma patients, leading to the standardization of a music-based assessment of awareness: Music Therapy Assessment Tool for Awareness in Disorders of Consciousness (MATADOC). CRS-R and MATADOC data were collected from 74 PDOC patients aged 16-70 years at three specialist centers in the USA, UK and Ireland. The data were analyzed by three ML techniques (neural networks, decision trees and cluster analysis) as well as modelled through system-level network analysis. RESULTS: PDOC diagnostic state can be predicted to a relatively high level of accuracy that sets a benchmark for future ML analysis using neurobehavioral data only. The outcomes of this study may also have implications for understanding the role of music therapy in interdisciplinary rehabilitation to help patients move from one coma state to another. CONCLUSIONS: This study has shown how ML can derive rules for diagnosis of PDOC with data from two neurobehavioral tools without the need to harvest large clinical and imaging datasets. Network analysis using the measures obtained from these two non-invasive tools provides novel, system-level ways of interpreting possible transitions between PDOC states, leading to possible use in novel, next-generation decision-support systems for PDOC.

Prediction of emergence from prolonged disorders of consciousness from measures within the UK rehabilitation outcomes collaborative database: a multicentre analysis using machine learning.

PURPOSE: Predicting emergence from prolonged disorders of consciousness (PDOC) is important for planning care and treatment. We used machine learning to examine which variables from routine clinical data on admission to specialist rehabilitation units best predict emergence by discharge. MATERIALS AND METHODS: A multicentre national cohort analysis of prospectively collected clinical data from the UK Rehabilitation Outcomes (UKROC) database 2010-2018. Patients (n = 1170) were operationally defined as "still in PDOC" or "emerged" by their total UK Functional Assessment Measure (FIM + FAM) discharge score. Variables included: Age, aetiology, length of stay, time since onset, and all items of the Neurological Impairment Scale, Rehabilitation Complexity Scale, Northwick Park Dependency Scale, and the Patient Categorisation Tool. After filtering, prediction of emergence was explored using four techniques: binary logistic regression, linear discriminant analysis, artificial neural networks, and rule induction. RESULTS: Triangulation through these techniques consistently identified characteristics associated with emergence from PDOC. More severe motor impairment, complex disability, medical and behavioural instability, and anoxic aetiology were predictive of non-emergence, whereas those with less severe motor impairment, agitated behaviour and complex disability were predictive of emergence. CONCLUSIONS: This initial exploration demonstrates the potential opportunities to enhance prediction of outcome using machine learning techniques to explore routinely collected clinical data. Implications for rehabilitationPredicting emergence from prolonged disorders of consciousness is important for planning care and treatment.Few evidence-based criteria exist for aiding clinical decision-making and existing criteria are mostly based upon acute admission data.Whilst acknowledging the limitations of using proxy data for diagnosis of emergence, this study suggests that key items from the UKROC dataset, routinely collected on admission to specialist rehabilitation some months post injury, may help to predict those patients who are more (or less) likely to regain consciousness.Machine learning can help to enhance our understanding of the best predictors of outcome and thus assist with clinical decision-making in PDOC.

Depression, anxiety and worry in young Pacific adults in New Zealand during the COVID-19 pandemic.

OBJECTIVE: To measure symptoms of anxiety, depression and hopelessness in a sample of young Pacific adults living in Auckland, New Zealand during the 2020/2021 COVID-19 pandemic and identify protective factors. METHODS: Participants were 267 Pacific adults (58% female) who completed a survey online. Analyses included descriptive statistics, correlations, linear regression and symptom network analysis. RESULTS: Around 25% of the sample scored in the range for moderate to severe anxiety and 10% for moderate to severe depression on standard measures. Almost 40% indicated that they found the first lockdown very stressful and 55% noted that some members of their family found it stressful. Only 16% worried about COVID-19 and their future quite a bit or constantly, while another 25% worried sometimes. Self-compassion and Pacific Identity had moderate, negative correlations, and Worry about COVID-19 had weak positive correlations, with anxiety, depression, hopelessness and perceived stress. CONCLUSION: These results suggest that, while the prevalence of depression and anxiety are quite high among this population, fostering ethnic identity and self-compassion in Pacific children and adolescents might protect against developing depression and anxiety.

A comparison of patient appraisal of professional skills for GPs in training participating in differing education programs.

BACKGROUND: Medical boards and healthcare providers internationally are coming under increasing pressure to attract international medical graduates (IMGs) and overseas trained doctors (OTDs) to cope with predicted general practice (GP) doctor shortages. Various pathways to registration are made available for this purpose. There is very little understanding of the effects of different training pathways to licensing and registration on the ability of IMGs and OTDs, as well as locally trained doctors, to acquire the desirable professional skills deemed necessary for working effectively in the primary care sector. METHODS: Feedback from patients was collected at the end of their scheduled consultation with their doctor using a questionnaire consisting of 13 Likert scale items that asked them to rate their experience of the consultation. Feedback was obtained for doctors going through the Royal Australian College of General Practice (RACGP) Practice Experience Program (PEP) and the Australian General Practice Training Program (AGPT), with the former intended primarily for IMGs and OTDs, and the latter for local medical graduates including from New Zealand. Patient feedback was also obtained for patients visiting already Fellowed and experienced GPs for comparative purposes, resulting in data for three groups of doctors (two trainee, one already Fellowed). Rater consistency and agreement measures, analysis of variance, principal component analysis, t-tests and psychometric network analysis were undertaken between and within groups to identify similarities and differences in patient experience and professionalism of doctors. RESULTS: There was a small but significant difference in average patient raw scores given to PEP and AGPT doctors (90.25, 90.97%), with the highest scores for 'Respect shown' (92.24, 93.15%) and the lowest for 'Reassurance' 89.38, 89.84%). Male patients gave lower scores (89.56%) than female patients (91.23%) for both groups of doctors. In comparison, patients gave experienced GPs an average 91.38% score, with male patients giving a lower average score than female patients (90.62, 91.93%). Two components were found in the patient data (interpersonal communication, caring/empathy) that account for over 80% of the variance. When patient scores were aggregated by doctor, the average PEP and AGPT doctor scores received were 90.27 and 90.99%, in comparison to the average experienced GP score of 91.43%. Network analysis revealed differences in the connectedness of items between these two groups as well as in comparison with experienced GPs, suggesting that PEP doctors' skills are less cohesively developed in the areas of listening ability, explaining and providing reassurance. CONCLUSIONS: The small but statistically significant differences between doctor groups reported in this preliminary study are supplemented by percentile analysis, network analysis and principal component analysis to identify areas for further exploration and study. There is scope for improving the integration of interpersonal communication skills of GPs in Training with their caring and empathy skills, when compared with experienced GPs as a benchmark. Suggestions are made for enhancing professional skills from a patients' perspective in future training programs.

Strengthening Community End-of-Life Care through Implementing Measurement-Based Palliative Care.

The increasing demand for palliative care in New Zealand presents a potential threat to the quality of service delivery. One strategy to overcome this is through the implementation of valid and reliable patient-reported outcome measures. This mixed-methods study aimed to (1) implement measurement-based palliative care (MBPC) in a community palliative care service in Auckland, New Zealand; (2) evaluate the clinical utility of MBPC perceived by clinicians; (3) describe patient characteristics as measured by the Integrated Palliative Care Outcome Scale (IPOS), the Australasian Modified Karnofsky Performance Scale (AKPS), and Phase of Illness (POI); and (4) evaluate the internal consistency of the IPOS. Participants were over 18 years of age from a community outpatient palliative care service. In a phased approach to implementation, healthcare staff were educated on each instrument used for patient assessment. Uptake and internal consistency were evaluated through descriptive statistics. An interpretive descriptive methodology was used to explore the clinical utility of MBPC through semi-structured interviews with seven clinical staff members. Individual patient assessments (n = 1507) were undertaken predominantly on admission, with decreasing frequency as patients advanced through to the terminal phase of their care. Mean total IPOS scores were 17.97 (SD = 10.39, α = 0.78). The POI showed that 65% of patients were in the stable phase, 20% were in the unstable phase, 9% were in the deteriorating phase, and 2% were in the terminal phase. Clinicians reported that MBPC facilitated holistic and comprehensive assessments, as well as the development of a common interdisciplinary language. Clinicians expressed discomfort using the psychosocial and spiritual items. Measurement-based palliative care was only partially implemented but it was valued by staff and perceived to increase the quality of service delivery. Future research should determine the optimal timing of assessments, cultural responsivity for Maori and Pacific patients, and the role of MBPC in decision support for clinicians.

Are there differences between SIMG surgeons and locally trained surgeons in Australia and New Zealand, as rated by colleagues and themselves?

BACKGROUND: Representation of specialist international medical graduates (SIMGs) in specific specialties such as surgery can be expected to grow as doctor shortages are predicted in the context of additional care provision for aging populations and limited local supply. Many national medical boards and colleges provide pathways for medical registration and fellowship of SIMGs that may include examinations and short-term training. There is currently very little understanding of how SIMGs are perceived by colleagues and whether their performance is perceived to be comparable to locally trained medical specialists. It is also not known how SIMGs perceive their own capabilities in comparison to local specialists. The aim of this study is to explore the relationships between colleague feedback and self-evaluation in the specialist area of surgery to identify possible methods for enhancing registration and follow-up training within the jurisdiction of Australia and New Zealand. METHODS: Feedback from 1728 colleagues to 96 SIMG surgeons and 406 colleagues to 25 locally trained Fellow surgeons was collected, resulting in 2134 responses to 121 surgeons in total. Additionally, 98 SIMGs and 25 Fellows provided self-evaluation scores (123 in total). Questionnaire and data reliability were calculated before analysis of variance, principal component analysis and network analysis were performed to identify differences between colleague evaluations and self-evaluations by surgeon type. RESULTS: Colleagues rated SIMGs and Fellows in the 'very good' to 'excellent' range. Fellows received a small but statistically significant higher average score than SIMGs, especially in areas dealing with medical skills and expertise. However, SIMGs received higher scores where there was motivation to demonstrate working well with colleagues. Colleagues rated SIMGs using one dimension and Fellows using three, which can be identified as clinical management skills, inter-personal communication skills and self-management skills. On self-evaluation, both SIMGs and Fellows gave themselves a significant lower average score than their colleagues, with SIMGs giving themselves a statistically significant higher score than Fellows. CONCLUSIONS: Colleagues rate SIMGs and Fellows highly. The results of this study indicate that SIMGs tend to self-assess more highly, but according to colleagues do not display the same level of differentiation between clinical management, inter-personal and self-management skills. Further research is required to confirm these provisional findings and possible reasons for lack of differentiation if this exists. Depending on the outcome, possible support mechanisms can be explored that may lead to increased comparable performance with locally trained graduates of Australia and New Zealand in these three dimensions.

Colleague appraisal of Australian general practitioners in training: an analysis of multisource feedback data.

BACKGROUND: Multisource feedback is an evidence-based and validated tool used to provide clinicians, including those in training, feedback on their professional and interpersonal skills. Multisource feedback is mandatory for participants in the Royal Australian College of General Practitioners Practice Experience Program and for some Australian General Practice Training Registrars. Given the recency of the Practice Experience Program, there are currently no benchmarks available for comparison within the program and to other comparable cohorts including doctors in the Australian General Practice Training program. The aim of this study is to evaluate and compare colleague feedback within and across General Practice trainee cohorts. METHODS: Colleague feedback, from multisource feedback of Practice Experience Program participants and Australian General Practice Training Registrars, collected between January 2018 and April 2020, was compared to identify similarities and differences. Analyses entailed descriptive statistics, between and within groups rater consistency and agreement measures, principal component analysis, t-tests, analysis of variance, and psychometric network analysis. RESULTS: Colleague ratings of Practice Experience Program participants (overall average 88.58%) were lower than for Registrars (89.08%), although this difference was not significant. 'Communication with patients' was rated significantly lower for Practice Experience Program participants (2.13%) while this group was rated significantly better for their 'Ability to say no' (1.78%). Psychometric network analyses showed stronger linkages between items making up the behavioural component (compared to the items of the performance and self-management components, as found by principal component analysis) for Practice Experience Program participants as compared to Registrars. Practice Experience Program participants were stronger in clinical knowledge and skills as well as confidentiality, while Registrars were stronger in communicating with patients, managing their own stress, and in their management and leadership skills. CONCLUSIONS: The multisource feedback scores of doctors undertaking the Practice Experience Program suggests that, while all mean values are 'very good' to 'excellent', there are areas for improvement. The linkages between skills suggests that Practice Experience Program doctors' skills are somewhat isolated and have yet to fully synthesise. We now have a better understanding of how different groups of General Practitioners in training compare with respect to professional and interpersonal skills. Based on the demonstrated differences, the Practice Experience Program might benefit from the addition of educational activities to target the less developed skills.

Risk factors for depression in Pacific adolescents in New Zealand: A network analysis.

BACKGROUND: Network analysis provides opportunities to gain a greater understanding of the complex interplay of risk factors for depression and heterogeneous symptom presentations. This study used network analysis to discover risk factors associated with both depression severity and depression symptoms amongst Pacific adolescents in New Zealand. METHODS: Mixed graphical models with regularization were fitted to data from a community sample of New Zealand born, Pacific adolescents, (n = 561; 51% male; Mean age (SD) = 17 (0.35)) and associations between a wide range of potentially explanatory variables and depression severity and depression symptoms investigated. The associations identified were then tested for reliability, using resampling techniques and sensitivity analysis. RESULTS: In the networks, the explanatory variables associated with both depression severity and depression symptoms were those related to quality of the relationships with mother or friends, school connectedness, and self-assessed weight, but the symptoms they were associated with varied substantially. In the depression severity networks, impulsivity appeared to be a bridging node connecting depression severity with delinquency and negative peer influence. LIMITATIONS: The data were analysed cross-sectionally, so causal inferences about the directions of relationships could not be inferred and most of the data were self-reported. CONCLUSIONS: The results illustrate the varied way that adolescent depression can manifest itself in terms of symptoms and suggest specific items on the depression inventory that might be suitable targets for prevention strategies and interventions, based on the risk factor - depression symptom profiles of individuals or groups.

Intelligent Palliative Care Based on Patient-Reported Outcome Measures.

CONTEXT: The growth of patient reported outcome measures data in palliative care provides an opportunity for machine learning to identify patterns in patient responses signifying different phases of illness. OBJECTIVES: The study will explore if machine learning and network analysis can identify phases in patient palliative status through symptoms reported on the Integrated Palliative Care Outcome Scale (IPOS). METHODS: A partly cross-sectional and partially longitudinal observational study was undertaken using the Australasian Karnofsky Performance Scale (AKPS); Integrated Palliative Care Outcome Scale (IPOS); Phase of Illness (POI). Patient palliative records (n = 1507, 65% stable, 20% unstable, 9% deteriorating, 2% terminal) from 804 adult patients enrolled in a New Zealand palliative care service were analysed using a combination of statistical, machine learning and network analysis techniques. RESULTS: Data from IPOS showed considerable variation with phase. Also, network analysis showed clear associations between items by phase. Six machine learning techniques identified the most important variables for predicting possible transition between phases of illness. Network analysis for all patients showed that Poor Appetite and Loss of Energy were central IPOS items, with Loss of Energy linked to Drowsiness, Shortness of Breath and Lack of Mobility on the one hand, and Poor Appetite linked to Nausea, Vomiting, Constipation and Sore and Dry Mouth on the other. CONCLUSION: These preliminary results, when coupled with the latest technological developments in mobile apps and wearable technology, could point the way to increased use of digital therapeutics in continuous palliative care monitoring.

Mobile Phone Use by People with Mild to Moderate Dementia: Uncovering Challenges and Identifying Opportunities: Mobile Phone Use by People with Mild to Moderate Dementia.

With the rising usage of mobile phones by people with mild dementia, and the documented barriers to technology use that exist for people with dementia, there is an open opportunity to study the specifics of mobile phone use by people with dementia. In this work we provide a first step towards filling this gap through an interview study with fourteen people with mild to moderate dementia. Our analysis yields insights into mobile phone use by people with mild to moderate dementia, challenges they experience with mobile phone use, and their ideas to address these challenges. Based on these findings, we discuss design opportunities to help achieve more accessible and supportive technology use for people with dementia. Our work opens up new opportunities for the design of systems focused on augmenting and enhancing the abilities of people with dementia.

A novel way to quantify schizophrenia symptoms in clinical trials.

BACKGROUND: A major problem in quantifying symptoms of schizophrenia is establishing a reliable distinction between enduring and dynamic aspects of psychopathology. This is critical for accurate diagnosis, monitoring and evaluating treatment effects in both clinical practice and trials. MATERIALS AND METHODS: We applied Generalizability Theory, a robust novel method to distinguish between dynamic and stable aspects of schizophrenia symptoms in the widely used Positive and Negative Symptom Scale (PANSS) using a longitudinal measurement design. The sample included 107 patients with chronic schizophrenia assessed using the PANSS at five time points over a 24-week period during a multi-site clinical trial of N-Acetylcysteine as an add-on to maintenance medication for the treatment of chronic schizophrenia. RESULTS: The original PANSS and its three subscales demonstrated good reliability and generalizability of scores (G = 0.77-0.93) across sample population and occasions making them suitable for assessment of psychosis risks and long-lasting change following a treatment, while subscales of the five-factor models appeared less reliable. The most enduring symptoms represented by the PANSS were poor attention, delusions, blunted affect and poor rapport. More dynamic symptoms with 40%-50% of variance explained by patient transient state including grandiosity, preoccupation, somatic concerns, guilt feeling and hallucinatory behaviour. CONCLUSIONS: Identified dynamic symptoms are more amendable to change and should be the primary target of interventions aiming at effectively treating schizophrenia. Separating out the dynamic symptoms would increase assay sensitivity in trials, reduce the signal to noise ratio and increase the potential to detect the effects of novel therapies in clinical trials.

Bibliometric mapping of intensive care nurses' wellbeing: development and application of the new iAnalysis model.

BACKGROUND: Intensive care nurse wellbeing is essential to a healthy healthcare workforce. Enhanced wellbeing has widespread benefits for workers. Bibliometrics enables quantitative analysis of bourgeoning online data. Here, a new model is developed and applied to explore empirical knowledge underpinning wellbeing and intensive care nurse wellbeing in terms of size and impact, disciplinary reach, and semantics. METHODS: Mixed methods bibliometric study. Firstly, a new model coined 'iAnalysis' was developed for the analysis of published data. Secondly, iAnalysis was applied in two studies to examine wellbeing and ICU nurse wellbeing. Study one explored data from a title search with search terms [wellbeing OR well-being], identifying 17,543 records with bibliographic data. This dataset included 20,526 keywords. Of the identified records, 10,715 full-text manuscripts were retrieved. Study two explored data from a topic search with search terms [(intensive OR critical) AND (nurs*) AND (wellbeing OR well-being)], identifying 383 records with bibliographic data. This dataset included 1223 author keywords. Of the identified records, 328 full-text manuscripts were retrieved. RESULTS: Once data were collected, for size and impact, WoS Clarivate Analytics™ and RStudio™ were used to explore publication dates, frequencies, and citation performance. For disciplinary reach, RStudio™ (with the Bibliometrics™ package & Vosviewer™ plugin) was used to explore the records in terms of country of publication, journal presence, and mapping of authors. For semantics, once the bibliographic data was imported to RStudio™ (with the Bibliometrics™ package & Vosviewer™ plugin) keyword co-occurrences were identified and visualised. Full-text manuscripts were imported to NVivo™ to explore word frequencies of both the keywords and full-text manuscripts using the word frequency search. For both studies, records were predominantly published in the past 5 years, in English language, and from USA. The highest keyword co-occurrence for study one was "health and well-being", and for study two, "family and model". CONCLUSIONS: Terms commonly associated with 'illbeing', as opposed to 'wellbeing', were highly prevalent in both study datasets, but more so in intensive care nurse wellbeing data. Intensive care nurse wellbeing was virtually absent in this literature. The iAnalysis model provided a practice-friendly tool to explore a large source of online published literature.

Correction to: Multisource feedback as part of the medical board of Australia's professional performance framework: outcomes from a preliminary study.

Following publication of the original article [1], the author reported that Fig. 4 was missing. This has now been corrected in the original article.

Multisource feedback as part of the Medical Board of Australia's Professional Performance Framework: outcomes from a preliminary study.

BACKGROUND: The recent introduction of the Professional Performance Framework by the Medical Board of Australia is intended to strengthen continuing professional development for the 100,000 or so medical practitioners in Australia. An important option within the Framework is the use of multisource feedback from patients, colleagues and self-evaluations to allow doctors to reflect on their performance and identify methods for self-improvement. The aim of this study is to explore the relationships between patient feedback, colleague feedback, and self-evaluation using the same questionnaires as used by patients and colleagues. METHODS: Feedback data for around 2000 doctors belonging to four different groups were collected through non-probability sampling from nearly 100,000 patients and 24,000 colleagues. Reliability analysis was performed using single measures intraclass coefficients, Cronbach' alpha and signal-to-noise ratios. Analysis of variance was used to identify significant differences in scores between items and sub-populations of doctors; principal component analysis involving Kaiser-Meyer-Olkin (KMO) sampling adequacy and Bartlett's test for sphericity was used to reveal components of doctor performance; and correlation analysis was used for identifying convergence between sets of scores from different sources. RESULTS: Patients rated doctors highest on respect shown and lowest on reassurance provided. Colleagues rated doctors highest on trustworthiness and lowest on ability to say 'no'. With regard to self-evaluation, doctors gave themselves lower scores on the patient questionnaire and the colleague questionnaire (10 and 12%, respectively) than they received from their patients and colleagues. There were weak but positive correlations between self-scores and scores received indicating some convergence of agreement, with doctors feeling more comfortable with self-evaluation from the perspective of patients than from colleagues. CONCLUSIONS: Supplementing patient and colleague feedback with self-evaluation may help doctors confirm for themselves areas for enhanced CPD through convergence. If self-evaluation is used, the colleague questionnaire may be sufficient, since aspects of clinical competence, management, communication and leadership as well as patient care can be addressed through colleague items. Mentoring of doctors in CPD should aim to make doctors feel more comfortable about being rated by colleagues to enhance convergence between self-scores and evaluations from the perspective of colleagues.

Distinguishing transient versus stable aspects of depression in New Zealand Pacific Island children using Generalizability Theory.

BACKGROUND: The distinction between temporary versus enduring or state/trait aspects of depression is important. More precise distinction would improve understanding of the aetiology of depression and those aspects most amenable to intervention thus identifying more homogeneous, dynamic targets for clinical trials. Generalizability Theory has been proposed as useful for disentangling state and trait components of psychopathology. METHODS: We applied Generalizability Theory to determine the relative contributions of temporary and enduring aspects of depression in a widely used screening measure of depression the - 10-item Children's Depression Inventory (CDI-10; Kovacs, 1985). Participants were children of Pacific Island descent living in New Zealand (n = 668). Data were collected at ages - 9, 11, and 14 years. RESULTS: The CDI-10 demonstrated acceptable generalizability across occasions (G = 0.79) with about one third of variance in total scores attributed to temporary and two thirds to more enduring aspects of depression. There were no other significant sources of error variance. Two items were identified as more sensitive than the remaining eight to more dynamic symptoms. LIMITATIONS: Studies with briefer test-retest intervals are warranted. Use of this Pacific Island cohort limits generalizability of findings to other cultures and ethnicities. No data were collected on whether participants had received intervention for depression. CONCLUSIONS: While the CDI-10 reliably measures both stable and transient aspects of depression in children, the scale does not permit clear distinction between them. We advocate application of Generalizability Theory for developing state/trait depression measures and determining which existing measures are most suitable for capturing modifiable features of depression.