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BackgroundDissociation may be important across many mental health disorders, but has been variously conceptualised and measured. We introduced a conceptualisation of a common type of dissociative experience, ‘felt sense of anomaly’ (FSA), and developed a corresponding measure, the Černis Felt Sense of Anomaly (ČEFSA) scale.AimsWe aimed to develop a short-form version of the ČEFSA that is valid for adolescent and adult respondents.MethodData were collected from 1031 adult NHS patients with psychosis and 932 adult and 1233 adolescent non-clinical online survey respondents. Local structural equation modelling (LSEM) was used to establish measurement invariance of items across the age range. Ant colony optimisation (ACO) was used to produce a 14-item short-form measure. Finally, the expected test score function derived from item response theory modelling guided the establishment of interpretive scoring ranges.ResultsLSEM indicated 25 items of the original 35-item ČEFSA were age invariant. They were also invariant across gender and clinical status. ACO of these items produced a 14-item short-form (ČEFSA-14) with excellent psychometric properties (CFI=0.992; TLI=0.987; RMSEA=0.034; SRMR=0.017; Cronbach’s alpha=0.92). Score ranges were established based on the expected test scores at approximately 0.7, 1.25, and 2.0 theta (equivalent to standard deviations above the mean). Scores of 29 and above may indicate elevated levels of FSA-dissociation.ConclusionsThe ČEFSA-14 is a psychometrically valid measure of FSA-dissociation for adolescents and adults. It can be used with clinical and non-clinical respondents. It could be used by clinicians as an initial tool to explore dissociation with their clients.

PURPOSE: To assess the psychometric properties of glaucoma-specific health-related quality of life (HRQoL) item banks (IBs), and explore their efficiency using computerized adaptive testing (CAT) simulations. METHODS: In this cross-sectional, clinical study, 300 Asian glaucoma patients answered 221 items within seven IBs: Ocular Comfort Symptoms (OS); Activity Limitation (AL); Lighting (LT); Mobility (MB); Glaucoma Management (GM); Psychosocial (PSY); and Work (WK). Rasch analysis was conducted to assess each IB's psychometric properties (e.g., item "fit" to the construct; unidimensionality) and a set of analytic performance criteria guiding decision making relating to retaining or dropping domains and items was employed. CAT simulations determined the mean number of items for 'high' and 'moderate' measurement precision (stopping rule: SEM 0.3 and 0.387, respectively). RESULTS: Participants' mean age was 67.2 ± 9.2 years (62% male; 87% Chinese). LT, MB, and GM displayed good psychometric properties overall. To optimize AL's psychometric properties, 16 items were deleted due to poor "fit", high missing data, item bias, low discrimination and/or a low clinical/patient importance rating. To resolve multidimensionality in PSY, we rehomed 16 items into a "Concern (CN)" domain. PSY and CN required further amendment, including collapsing of response categories, and removal of poorly functioning items (N = 7). Due to poor measurement precision, low applicability and high ceiling effect, low test information indices, and low item separation index the WK IB was not considered further. In CAT simulations on the final seven IBs (n = 182 items total), an average of 12.1 and 15.7 items per IB were required for moderate and high precision measurement, respectively. CONCLUSIONS: After reengineering our seven IBs, they displayed robust psychometric properties and good efficiency in CAT simulations. Once finalized, GlauCAT™-Asian may enable comprehensive assessment of the HRQoL impact of glaucoma and associated treatments.

Patients with underlying comorbidities are particularly vulnerable to poor outcomes from SARS-CoV-2 infection. Despite the context-specific nature of vaccine hesitancy, there are currently no scales that incorporate disease or treatment-related hesitancy factors. We developed a six-item scale assessing disease-related COVID-19 vaccine attitudes and concerns (The Disease Influenced COVID-19 Vaccine Acceptance Scale-Six: DIVAS-6). A survey incorporating the DIVAS-6 was completed by 4683 participants with severe and/or chronic illness (3560 cancer; 842 diabetes; 281 multiple sclerosis (MS)). The survey included the Oxford COVID-19 Vaccine Hesitancy Scale, the Oxford COVID-19 Vaccine Confidence and Complacency Scale, demographic, disease-related, and vaccination status questions. The six items loaded onto two factors (disease complacency and vaccine vulnerability) using exploratory factor analysis and exploratory structural equation modeling. The two factors were internally consistent. Measurement invariance analysis showed the two factors displayed psychometric equivalence across the patient groups. Each factor significantly correlated with the two Oxford COVID-19 Vaccine scales, showing convergent validity. The summary score showed acceptable ability to discriminate vaccination status across diseases, with the total sample providing good-to-excellent discriminative ability. The DIVAS-6 has two factors measuring COVID-19 vaccine attitudes and concerns relating to potential complications of SARS-CoV-2 infection due to underlying disease (disease complacency) and vaccine-related impact on disease progression and treatment (vaccine vulnerability). This is the first validated scale to measure disease-related COVID-19 vaccine concerns and has been validated in people with cancer, diabetes, and MS. It is quick to administer and should assist with guiding information delivery about COVID-19 vaccination in medically vulnerable populations.

BackgroundWhen vaccination depends on injection, it is plausible that the blood-injection-injury cluster of fears may contribute to hesitancy. Our primary aim was to estimate in the UK adult population the proportion of COVID-19 vaccine hesitancy explained by blood-injection-injury fears.MethodsIn total, 15 014 UK adults, quota sampled to match the population for age, gender, ethnicity, income and region, took part (19 January–5 February 2021) in a non-probability online survey. The Oxford COVID-19 Vaccine Hesitancy Scale assessed intent to be vaccinated. Two scales (Specific Phobia Scale-blood-injection-injury phobia and Medical Fear Survey–injections and blood subscale) assessed blood-injection-injury fears. Four items from these scales were used to create a factor score specifically for injection fears.ResultsIn total, 3927 (26.2%) screened positive for blood-injection-injury phobia. Individuals screening positive (22.0%) were more likely to report COVID-19 vaccine hesitancy compared to individuals screening negative (11.5%), odds ratio = 2.18, 95% confidence interval (CI) 1.97–2.40, p < 0.001. The population attributable fraction (PAF) indicated that if blood-injection-injury phobia were absent then this may prevent 11.5% of all instances of vaccine hesitancy, AF = 0.11; 95% CI 0.09–0.14, p < 0.001. COVID-19 vaccine hesitancy was associated with higher scores on the Specific Phobia Scale, r = 0.22, p < 0.001, Medical Fear Survey, r = 0.23, p = r = 0.25, p < 0.001. Injection fears were higher in youth and in Black and Asian ethnic groups, and explained a small degree of why vaccine hesitancy is higher in these groups.ConclusionsAcross the adult population, blood-injection-injury fears may explain approximately 10% of cases of COVID-19 vaccine hesitancy. Addressing such fears will likely improve the effectiveness of vaccination programmes.

The Unmind Index is a 26-item, 7-subscale measure of mental health and wellbeing designed for use on the Unmind digital workplace mental health platform. The Unmind Index was developed and validated in the UK but is used internationally. This paper reports further psychometric validation of this measure for use in USA, Australia, and New Zealand.Participants in four countries completed the Unmind Index and a battery of existing measures. In Study 1, we use exploratory factor analysis to select questions for the Unmind Index and identify factor structure. In Study 2, we use confirmatory factor analysis to validate the Unmind Index.Study 1 establishes the factor structure, reliability, convergent and discriminant validity, and measurement invariance by age and gender of the Unmind Index separately for USA and for Australia/New Zealand. Study 2 further demonstrates measurement invariance across these locations as well as the UK, and establishes benchmark scores by location, age, and gender.We conclude that the Unmind Index is valid and reliable as a measure of mental health and wellbeing in these locations.

Background Automated virtual reality therapies are being developed to increase access to psychological interventions. We assessed the experience with one such therapy of patients diagnosed with psychosis, including satisfaction, side effects, and positive experiences of access to the technology. We tested whether side effects affected therapy. Methods In a clinical trial 122 patients diagnosed with psychosis completed baseline measures of psychiatric symptoms, received gameChange VR therapy, and then completed a satisfaction questionnaire, the Oxford-VR Side Effects Checklist, and outcome measures. Results 79 (65.8%) patients were very satisfied with VR therapy, 37 (30.8%) were mostly satisfied, 3 (2.5%) were indifferent/mildly dissatisfied, and 1 (0.8%) person was quite dissatisfied. The most common side effects were: difficulties concentrating because of thinking about what might be happening in the room (n = 17, 14.2%); lasting headache (n = 10, 8.3%); and the headset causing feelings of panic (n = 9, 7.4%). Side effects formed three factors: difficulties concentrating when wearing a headset, feelings of panic using VR, and worries following VR. The occurrence of side effects was not associated with number of VR sessions, therapy outcomes, or psychiatric symptoms. Difficulties concentrating in VR were associated with slightly lower satisfaction. VR therapy provision and engagement made patients feel: proud (n = 99, 81.8%); valued (n = 97, 80.2%); and optimistic (n = 96, 79.3%). Conclusions Patients with psychosis were generally very positive towards the VR therapy, valued having the opportunity to try the technology, and experienced few adverse effects. Side effects did not significantly impact VR therapy. Patient experience of VR is likely to facilitate widespread adoption.

ObjectiveTo examine vaccine uptake, hesitancy and explanatory factors amongst people with serious and/or chronic health conditions, including the impact of underlying disease on attitudes to vaccination.DesignCross-sectional survey.SettingTen Australian health services.Participants4683 patients (3560 cancer, 842 diabetes and 281 multiple sclerosis) receiving care at the health services participated in the 42-item survey, between June 30 to October 5, 2021.Main outcome measuresSociodemographic and disease-related characteristics, COVID-19 vaccine uptake, and the scores of three validated scales which measured vaccine hesitancy and vaccine-related beliefs generally and specific to the participants’ disease, including the Oxford COVID-19 Vaccine Hesitancy Scale, the Oxford COVID-19 Vaccine Confidence and Complacency Scale and the Disease Influenced Vaccine Acceptance Scale. Multivariable logistic regression was used to determine the associations between scale scores and vaccine uptake.ResultsOf all participants, 81.5% reported having at least one COVID-19 vaccine. Unvaccinated status was associated with younger age, female sex, lower education and income, English as a second language, and residence in regional areas (all pConclusionsDisease-specific concerns impact COVID-19 vaccine-related behaviours and beliefs in people with serious and/or chronic health conditions. This highlights the need to develop targeted strategies and education about COVID-19 vaccination to support medically vulnerable populations and health professionals.Trial registrationACTRN12621001467820

BACKGROUND Unmind is a workplace, digital, mental health platform with tools to help users track, maintain, and improve their mental health and well-being (MHWB). Psychological measurement plays a key role on this platform, providing users with insights on their current MHWB, the ability to track it over time, and personalized recommendations, while providing employers with aggregate information about the MHWB of their workforce. OBJECTIVE Due to the limitations of existing measures for this purpose, we aimed to develop and validate a novel well-being index for digital use, to capture symptoms of common mental health problems and key aspects of positive well-being. METHODS In Study 1A, questionnaire items were generated by clinicians and screened for face validity. In Study 1B, these items were presented to a large sample (n=1104) of UK adults, and exploratory factor analysis was used to reduce the item pool and identify coherent subscales. In Study 2, the final measure was presented to a new nationally representative UK sample (n=976), along with a battery of existing measures, with 238 participants retaking the Umind Index after 1 week. The factor structure and measurement invariance of the Unmind Index was evaluated using confirmatory factor analysis, convergent and discriminant validity by estimating correlations with existing measures, and reliability by examining internal consistency and test-retest intraclass correlations. RESULTS Studies 1A and 1B yielded a 26-item measure with 7 subscales: Calmness, Connection, Coping, Happiness, Health, Fulfilment, and Sleep. Study 2 showed that the Unmind Index is fitted well by a second-order factor structure, where the 7 subscales all load onto an overall MHWB factor, and established measurement invariance by age and gender. Subscale and total scores correlate well with existing mental health measures and generally diverge from personality measures. Reliability was good or excellent across all subscales. CONCLUSIONS The Unmind Index is a robust measure of MHWB that can help to identify target areas for intervention in nonclinical users of a mental health app. We argue that there is value in measuring mental ill health and mental well-being together, rather than treating them as separate constructs.

BACKGROUND There are two philosophical approaches to contemporary psychometrics: Rasch measurement theory (RMT) and item response theory (IRT). Either measurement strategy can be applied to computerized adaptive testing (CAT). There are potential benefits of IRT over RMT with regards to measurement precision, but also potential risks to measurement generalizability. RMT CAT assessments have demonstrated good performance with the CLEFT-Q, a patient-reported outcome measure for use in orofacial clefting. OBJECTIVE To test whether the post-hoc application of IRT (graded response models, GRMs, and multidimensional GRMs) to RMT-validated CLEFT-Q appearance scales could improve CAT accuracy at given assessment lengths. METHODS Partial credit Rasch models, unidimensional GRMs and a multidimensional GRM were calibrated for each of the 7 CLEFT-Q appearance scales (which measure the appearance of the: face, jaw, teeth, nose, nostrils, cleft lip scar and lips) using data from the CLEFT-Q field test. A second, simulated dataset was generated with 1000 plausible response sets to each scale. Rasch and GRM scores were calculated for each simulated response set, scaled to 0-100 scores, and compared by Pearson’s correlation coefficient, root mean square error (RMSE), mean absolute error (MAE) and 95% limits of agreement. For the face, teeth and jaw scales, we repeated this in a an independent, real patient dataset. We then used the simulated data to compare the performance of a range of fixed-length CAT assessments that were generated with partial credit Rasch models, unidimensional GRMs and the multidimensional GRM. Median standard error of measurement (SEM) was recorded for each assessment. CAT scores were scaled to 0-100 and compared to linear assessment Rasch scores with RMSE, MAE and 95% limits of agreement. This was repeated in the independent, real patient dataset with the RMT and unidimensional GRM CAT assessments for the face, teeth and jaw scales to test the generalizability of our simulated data analysis. RESULTS Linear assessment scores generated by Rasch models and unidimensional GRMs showed close agreement, with RMSE ranging from 2.2 to 6.1, and MAE ranging from 1.5 to 4.9 in the simulated dataset. These findings were closely reproduced in the real patient dataset. Unidimensional GRM CAT algorithms achieved lower median SEM than Rasch counterparts, but reproduced linear assessment scores with very similar accuracy (RMSE, MAE and 95% limits of agreement). The multidimensional GRM had poorer accuracy than the unidimensional models at comparable assessment lengths. CONCLUSIONS Partial credit Rasch models and GRMs produce very similar CAT scores. GRM CAT assessments achieve a lower SEM, but this does not translate into better accuracy. Commonly used SEM heuristics for target measurement reliability should not be generalized across CAT assessments built with different psychometric models. In this study, a relatively parsimonious multidimensional GRM CAT algorithm performed more poorly than unidimensional GRM comparators.

Genome sequencing (GS) is increasingly being used to diagnose rare diseases in paediatric patients; however, no measures exist to evaluate their knowledge of this technology. We aimed to develop a robust measure of knowledge of GS (the kids‐KOGS') suitable for use in the paediatric setting as well as for general public education. The target age was 11 to 15 year olds. An iterative process involving six sequential stages was conducted to develop a set of draft true/false items. These were then administered to 539 target‐age school pupils (mean 12.8; SD ± 1.3), from the United Kingdom. Item‐response theory was used to confirm the psychometric suitability of the candidate items. None of the Items was identified as misfits. All 10 items performed well under the two‐parameter logistic model. The internal consistency of the test was 0.84 (Cronbach alpha value) indicating excellent reliability. The mean kids‐KOGS score in the sample overall was 4.24 (SD; 2.49), where 0 = low knowledge and 10 = high knowledge. Age was positively associated with score in a multivariate linear regression. The kids‐KOGS is a short and reliable tool that can be used by researchers and healthcare professionals offering GS to paediatric patients. Further validation in a clinical setting is required., The final 10‐item Knowledge of Genome Sequencing Measure for Young People (kids‐KOGS).

Planning is an essential cognitive process of executive functions and is considered as one of the most important brain functions. Planning has been extensively studied in the field of neuropsychology, but there is a lack of computerized assessment tools of planning ability that are easily accessible to researchers and clinicians. The present study aims to validate a newly developed online spatial network test that is designed for both clinical and nonclinical populations. Exploratory factor analysis revealed a unidimensional factorial structure with moderate to high internal consistency in the test. Two-parameter logistic item response theory analysis showed acceptable item and model fit and no violation of the local independence assumption. The overall success rate exhibited a positive correlation with the performance of planning before attempting to solve the items. After correcting for attenuation, moderate to high correlations were found between the spatial network test and the International Cognitive Ability Resource 16 short form cognitive ability test and the automated perceptual maze test, respectively, demonstrating convergent, discriminant, and criterion validity. Future directions and potential applications of the spatial network test as an assessment tool to measure planning for researchers and clinicians are discussed in the end.

Background: Vaccination is the cornerstone of the global public health response to the COVID-19 pandemic. Excess morbidity and mortality of COVID-19 infection is seen in people with cancer. COVID-19 vaccine hesitancy has been observed in this medically vulnerable population, although associated attitudes and beliefs remain poorly understood. Methods: An online cross-sectional survey of people with solid organ cancers was conducted through nine health services across Australia. Demographics, cancer-related characteristics and vaccine uptake were collected. Perceptions and beliefs regarding COVID-19 vaccination were assessed using the Oxford COVID-19 Vaccine Hesitancy Scale, the Oxford COVID-19 Vaccine Confidence and Complacency Scale and the Disease Influenced Vaccine Acceptance Scale-6. Results: Between June and October 2021, 2691 people with solid organ cancers completed the survey. The median age was 62.5 years (SD = 11.8; range 19–95), 40.9% were male, 71.3% lived in metropolitan areas and 90.3% spoke English as their first language. The commonest cancer diagnoses were breast (36.6%), genitourinary (18.6%) and gastrointestinal (18.3%); 59.2% had localized disease and 56.0% were receiving anti-cancer therapy. Most participants (79.7%) had at least one COVID-19 vaccine dose. Vaccine uptake was higher in people who were older, male, metropolitan, spoke English as a first language and had a cancer diagnosis for more than six months. Vaccine hesitancy was higher in people who were younger, female, spoke English as a non-dominant language and lived in a regional location, and lower in people with genitourinary cancer. Vaccinated respondents were more concerned about being infected with COVID-19 and less concerned about vaccine safety and efficacy. Conclusions: People with cancer have concerns about acquiring COVID-19, which they balance against vaccine-related concerns about the potential impact on their disease progress and/or treatment. Detailed exploration of concerns in cancer patients provides valuable insights, both for discussions with individual patients and public health messaging for this vulnerable population.

[EN] Success in all sorts of situations is the most classical interpretation of general intelligence. Under limited resources, however, the capability of an agent must necessarily be limited too, and generality needs to be understood as comprehensive performance up to a level of difficulty. The degree of generality then refers to the way an agent's capability is distributed as a function of task difficulty. This dissects the notion of general intelligence into two non-populational measures, generality and capability, which we apply to individuals and groups of humans, other animals and AI systems, on several cognitive and perceptual tests. Our results indicate that generality and capability can decouple at the individual level: very specialised agents can show high capability and vice versa. The metrics also decouple at the population level, and we rarely see diminishing returns in generality for those groups of high capability. We relate the individual measure of generality to traditional notions of general intelligence and cognitive efficiency in humans, collectives, non-human animals and machines. The choice of the difficulty function now plays a prominent role in this new conception of generality, which brings a quantitative tool for shedding light on long-standing questions about the evolution of general intelligence and the evaluation of progress in Artificial General Intelligence., We are grateful to Jim DiCarlo and his lab (http://dicarlolab.mit.edu/, MIT), Judith Burkart, Laura Damerius and Carel van Schaik (Universitat Zurich), and Katherine Bruce and Mark Galizio (UNC Wilmington), for their data and the very helpful discussions. We thank David Stillwell for suggesting the relation between person-fit metrics and an early version of generality, and Heinrich Peters for pointing out Guttman's model. JHO has been partially supported by the EU (FEDER) and Spanish MINECO grant RTI2018-094403-B-C32 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe", Generalitat Valenciana under grant PROMETEO/2019/098 and EU's Horizon 2020 research and innovation programme under grant agreement No. 952215 (TAILOR). JHO and SOH are funded by the Future of Life Institute, FLI, under grant RFP2-152. JHO and LC are funded by US DARPA HR00112120007 (RECoG-AI). LC and SOH are funded by the Leverhulme Trust through a grant for the Leverhulme Centre for the Future of Intelligence. FMP is funded by the AI-Watch and HUMAINT projects by DG CONNECT and DG JRC of the European Commission. All authors declare no competing interests. We thank John Burden, Richard Evans and the anonymous reviewers for their insightful comments and corrections. Code and data are available for reproducibility at http://github.com/jorallo/gener ality as explained in G.

UNSTRUCTURED Patient-reported assessments are transforming many facets of health care, but there is scope to modernize their delivery. Contemporary assessment techniques like computerized adaptive testing (CAT) and machine learning can be applied to patient-reported assessments to reduce burden on both patients and health care professionals; improve test accuracy; and provide individualized, actionable feedback. The Concerto platform is a highly adaptable, secure, and easy-to-use console that can harness the power of CAT and machine learning for developing and administering advanced patient-reported assessments. This paper introduces readers to contemporary assessment techniques and the Concerto platform. It reviews advances in the field of patient-reported assessment that have been driven by the Concerto platform and explains how to create an advanced, adaptive assessment, for free, with minimal prior experience with CAT or programming.

Patient-reported assessments are transforming many facets of health care, but there is scope to modernize their delivery. Contemporary assessment techniques like computerized adaptive testing (CAT) and machine learning can be applied to patient-reported assessments to reduce burden on both patients and health care professionals; improve test accuracy; and provide individualized, actionable feedback. The Concerto platform is a highly adaptable, secure, and easy-to-use console that can harness the power of CAT and machine learning for developing and administering advanced patient-reported assessments. This paper introduces readers to contemporary assessment techniques and the Concerto platform. It reviews advances in the field of patient-reported assessment that have been driven by the Concerto platform and explains how to create an advanced, adaptive assessment, for free, with minimal prior experience with CAT or programming.

Background Unmind is a workplace, digital, mental health platform with tools to help users track, maintain, and improve their mental health and well-being (MHWB). Psychological measurement plays a key role on this platform, providing users with insights on their current MHWB, the ability to track it over time, and personalized recommendations, while providing employers with aggregate information about the MHWB of their workforce. Objective Due to the limitations of existing measures for this purpose, we aimed to develop and validate a novel well-being index for digital use, to capture symptoms of common mental health problems and key aspects of positive well-being. Methods In Study 1A, questionnaire items were generated by clinicians and screened for face validity. In Study 1B, these items were presented to a large sample (n=1104) of UK adults, and exploratory factor analysis was used to reduce the item pool and identify coherent subscales. In Study 2, the final measure was presented to a new nationally representative UK sample (n=976), along with a battery of existing measures, with 238 participants retaking the Umind Index after 1 week. The factor structure and measurement invariance of the Unmind Index was evaluated using confirmatory factor analysis, convergent and discriminant validity by estimating correlations with existing measures, and reliability by examining internal consistency and test-retest intraclass correlations. Results Studies 1A and 1B yielded a 26-item measure with 7 subscales: Calmness, Connection, Coping, Happiness, Health, Fulfilment, and Sleep. Study 2 showed that the Unmind Index is fitted well by a second-order factor structure, where the 7 subscales all load onto an overall MHWB factor, and established measurement invariance by age and gender. Subscale and total scores correlate well with existing mental health measures and generally diverge from personality measures. Reliability was good or excellent across all subscales. Conclusions The Unmind Index is a robust measure of MHWB that can help to identify target areas for intervention in nonclinical users of a mental health app. We argue that there is value in measuring mental ill health and mental well-being together, rather than treating them as separate constructs.

BackgroundAdolescence can be a challenging time, characterized by self-consciousness, heightened regard for peer acceptance, and fear of rejection. Interpersonal concerns are amplified by unpredictable social interactions, both online and offline. This developmental and social context is potentially conducive to the emergence of paranoia. However, research on paranoia during adolescence is scarce.MethodOur aim was to examine the prevalence, structure, and probabilistic causal mechanisms of adolescent paranoia. A representative school cohort of 801 adolescents (11–15 y) completed measures of paranoia and a range of affective, cognitive, and social factors. A Bayesian approach with Directed Acyclic Graphs (DAGs) was used to assess the causal interactions with paranoia.ResultsParanoid thoughts were very common, followed a continuous distribution, and were hierarchically structured. There was an overall paranoia factor, with sub-factors of social fears, physical threat fears, and conspiracy concerns. With all other variables controlled, DAG analysis identified paranoia had dependent relationships with negative affect, peer difficulties, bullying, and cognitive-affective responses to social media. The causal directions could not be fully determined, but it was more likely that negative affect contributed to paranoia and paranoia impacted peer relationships. Problematic social media use did not causally influence paranoia.ConclusionsThere is a continuum of paranoia in adolescence and occasional suspicions are common at this age. Anxiety and depression are closely connected with paranoia and may causally contribute to its development. Paranoia may negatively impact adolescent peer relationships. The clinical significance of paranoia in adolescents accessing mental health services must now be established.

BackgroundThe Green et al., Paranoid Thoughts Scale (GPTS) – comprising two 16-item scales assessing ideas of reference (Part A) and ideas of persecution (Part B) – was developed over a decade ago. Our aim was to conduct the first large-scale psychometric evaluation.MethodsIn total, 10 551 individuals provided GPTS data. Four hundred and twenty-two patients with psychosis and 805 non-clinical individuals completed GPTS Parts A and B. An additional 1743 patients with psychosis and 7581 non-clinical individuals completed GPTS Part B. Factor analysis, item response theory, and receiver operating characteristic analyses were conducted.ResultsThe original two-factor structure of the GPTS had an inadequate model fit: Part A did not form a unidimensional scale and multiple items were locally dependant. A Revised-GPTS (R-GPTS) was formed, comprising eight-item ideas of reference and 10-item ideas of persecution subscales, which had an excellent model fit. All items in the new Reference (a = 2.09–3.67) and Persecution (a = 2.37–4.38) scales were strongly discriminative of shifts in paranoia and had high reliability across the spectrum of severity (a > 0.90). The R-GPTS score ranges are: average (Reference: 0–9; Persecution: 0–4); elevated (Reference: 10–15; Persecution: 5–10); moderately severe (Reference: 16–20; Persecution:11–17); severe (Reference: 21–24; Persecution: 18–27); and very severe (Reference: 25+; Persecution: 28+). Recommended cut-offs on the persecution scale are 11 to discriminate clinical levels of persecutory ideation and 18 for a likely persecutory delusion.ConclusionsThe psychometric evaluation indicated a need to improve the GPTS. The R-GPTS is a more precise measure, has excellent psychometric properties, and is recommended for future studies of paranoia.

Background Precise assessment tools for psychotic experiences in young people may help identify symptoms early and facilitate advances in treatment. In this study we provide an exemplar - with a paranoia scale for youth – for improving measurement precision for psychotic experiences using item response theory (IRT). We evaluate the psychometric properties of the new measure, test for measurement invariance, and assess its potential for computerised adaptive testing (CAT). Method The 18-item Bird Checklist of Adolescent Paranoia (B-CAP) was completed by 1102 adolescents including 301 patients with mental health problems and 801 from the general population. After excluding outliers (n = 10), IRT was used to examine item properties, test reliability, and measurement invariance. The properties of an adaptive B-CAP were assessed using a simulation of 10,000 responses. Results All B-CAP items were highly discriminative (a = 1.14–2.77), whereby small shifts in paranoia led to a higher probability of item endorsement. Test reliability was high (a > 0.90) across a wide range of paranoia severity (θ = −0.45–3.36), with the greatest precision at elevated levels. All items were invariant for gender, age, and population groups. The simulated adaptive B-CAP performed with high accuracy and required only 5–6 items at higher levels of paranoia severity. Conclusions The B-CAP is a reliable assessment tool with excellent psychometric properties to assess both non-clinical and clinical levels of paranoia in young people, with potential as an efficient adaptive test. In future, these approaches could be used to develop a multidimensional CAT to assess the full range of psychotic experiences in youth.

Summary Background The effectiveness of the COVID-19 vaccination programme depends on mass participation: the greater the number of people vaccinated, the less risk to the population. Concise, persuasive messaging is crucial, particularly given substantial levels of vaccine hesitancy in the UK. Our aim was to test which types of written information about COVID-19 vaccination, in addition to a statement of efficacy and safety, might increase vaccine acceptance. Methods For this single-blind, parallel-group, randomised controlled trial, we aimed to recruit 15 000 adults in the UK, who were quota sampled to be representative. Participants were randomly assigned equally across ten information conditions stratified by level of vaccine acceptance (willing, doubtful, or strongly hesitant). The control information condition comprised the safety and effectiveness statement taken from the UK National Health Service website; the remaining conditions addressed collective benefit, personal benefit, seriousness of the pandemic, and safety concerns. After online provision of vaccination information, participants completed the Oxford COVID-19 Vaccine Hesitancy Scale (outcome measure; score range 7–35) and the Oxford Vaccine Confidence and Complacency Scale (mediation measure). The primary outcome was willingness to be vaccinated. Participants were analysed in the groups they were allocated. p values were adjusted for multiple comparisons. The study was registered with ISRCTN, ISRCTN37254291. Findings From Jan 19 to Feb 5, 2021, 15 014 adults were recruited. Vaccine hesitancy had reduced from 26·9% the previous year to 16·9%, so recruitment was extended to Feb 18 to recruit 3841 additional vaccine-hesitant adults. 12 463 (66·1%) participants were classified as willing, 2932 (15·6%) as doubtful, and 3460 (18·4%) as strongly hesitant (ie, report that they will avoid being vaccinated for as long as possible or will never get vaccinated). Information conditions did not alter COVID-19 vaccine hesitancy in those willing or doubtful (adjusted p values >0·70). In those strongly hesitant, COVID-19 vaccine hesitancy was reduced, in comparison to the control condition, by personal benefit information (mean difference –1·49, 95% CI –2·16 to –0·82; adjusted p=0·0015), directly addressing safety concerns about speed of development (−0·91, –1·58 to –0·23; adjusted p=0·0261), and a combination of all information (−0·86, –1·53 to –0·18; adjusted p=0·0313). In those strongly hesitant, provision of personal benefit information reduced hesitancy to a greater extent than provision of information on the collective benefit of not personally getting ill (−0·97, 95% CI –1·64 to –0·30; adjusted p=0·0165) or the collective benefit of not transmitting the virus (−1·01, –1·68 to –0·35; adjusted p=0·0150). Ethnicity and gender were found to moderate information condition outcomes. Interpretation In the approximately 10% of the population who are strongly hesitant about COVID-19 vaccines, provision of information on personal benefit reduces hesitancy to a greater extent than information on collective benefits. Where perception of risk from vaccines is most salient, decision making becomes centred on the personal. As such, messaging that stresses the counterbalancing personal benefits is likely to prove most effective. The messaging from this study could be used in public health communications. Going forwards, the study highlights the need for future health campaigns to engage with the public on the terrain that is most salient to them. Funding National Institute for Health Research (NIHR) Oxford Biomedical Research Centre and NIHR Oxford Health Biomedical Research Centre.

Purpose: Evaluate efficiency, precision, and validity of RetCAT, which comprises ten diabetic retinopathy (DR) quality of life (QoL) computerized adaptive tests (CATs). Methods: In this cross-sectional clinical study, 183 English and/or Mandarin-speaking participants with DR (mean age ± standard deviation [SD] 56.4 ± 11.9 years; 38% proliferative DR [worse eye]) were recruited from retinal clinics in Singapore. Participants answered the RetCAT tests (Symptoms, Activity Limitation, Mobility, Emotional, Health Concerns, Social, Convenience, Economic, Driving, and Lighting), which were capped at seven items each, and other questionnaires, and underwent eye tests. Our primary evaluation focused on RetCAT efficiency (i.e. standard error of measurement [SEM] ± SD achieved and time needed to complete each CAT). Secondary evaluations included an assessment of RetCAT's test precision and validity Results: Mean SEM across all RetCAT tests was 0.351, ranging from 0.272 ± 0.130 for Economic to 0.484 ± 0.130 for Emotional. Four tests (Mobility, Social, Convenience, and Driving) had a high level of measurement error. The median time to take each RetCAT test was 1.79 minutes, ranging from 1.12 (IQR [interquartile range] 1.63) for Driving to 3.28 (IQR 2.52) for Activity Limitation. Test precision was highest for participants at the most impaired end of the spectrum. Most RetCAT tests displayed expected correlations with other scales (convergent/divergent validity) and were sensitive to DR and/or vision impairment severity levels (criterion validity) Conclusions: RetCAT can provide efficient, precise, and valid measurement of DR-related QoL impact. Future application of RetCAT will employ a stopping rule based on SE rather than number of items to ensure that all tests can detect meaningful differences in person abilities. Responsiveness of RetCAT to treatment interventions must also be determined Translational Relevance: RetCAT may be useful for measuring the patient-centered impact of DR severity and disease progression and evaluating the effectiveness of new therapies Refereed/Peer-reviewed

We present nine facets for the analysis of the past and future evolution of AI. Each facet has also a set of edges that can summarise different trends and contours in AI. With them, we first conduct a quantitative analysis using the information from two decades of AAAI/IJCAI conferences and around 50 years of documents from AI topics, an official database from the AAAI, illustrated by several plots. We then perform a qualitative analysis using the facets and edges, locating AI systems in the intelligence landscape and the discipline as a whole. This analytical framework provides a more structured and systematic way of looking at the shape and boundaries of AI.

Objective Whole-genome sequencing is being implemented in research and clinical care, yet tools to assess patients’ knowledge are lacking. Our aim was to develop a robust measure of whole-genome sequencing knowledge suitable for patients and other stakeholders including research participants, public, students, and healthcare professionals. Methods An initial set of 17 items was developed via an iterative process including literature review, expert consultation, focus groups, and cognitive interviews with patients, and then administered to 243 individuals. We used exploratory factor analysis and item-response theory to confirm the psychometric suitability of the candidate items for assessing whole-genome sequencing knowledge. Results There was a strong main component after removing 5 items with low factor loadings. Item and scale homogeneity was achieved using Mokken scale analysis. Three further items were removed because they were misfits, inverse duplicates or resulted in local dependency. The remaining nine items fitted the two-parameter logistic IRT model which achieved excellent fit to the observed data. Cronbach’s alpha was 0.79 indicating acceptable reliability. Conclusion The KOGS, developed using a rigorous psychometric approach, is a brief and reliable tool. Practice implications The KOGS may prove useful for researchers and healthcare professionals using whole-genome sequencing with patients and other stakeholders.

This study investigates the item properties of a newly developed Automatic Number Series Item Generator (ANSIG). The foundation of the ANSIG is based on five hypothesised cognitive operators. Thirteen item models were developed using the numGen R package and eleven were evaluated in this study. The 16-item ICAR (International Cognitive Ability Resource1) short form ability test was used to evaluate construct validity. The Rasch Model and two Linear Logistic Test Model(s) (LLTM) were employed to estimate and predict the item parameters. Results indicate that a single factor determines the performance on tests composed of items generated by the ANSIG. Under the LLTM approach, all the cognitive operators were significant predictors of item difficulty. Moderate to high correlations were evident between the number series items and the ICAR test scores, with high correlation found for the ICAR Letter-Numeric-Series type items, suggesting adequate nomothetic span. Extended cognitive research is, nevertheless, essential for the automatic generation of an item pool with predictable psychometric properties., Journal of Intelligence;Jg.: 6, Heft: 2 | S. 20-1-20-25

New types of artificial intelligence (AI), from cognitive assistants to social robots, are challenging meaningful comparison with other kinds of intelligence. How can such intelligent systems be catalogued, evaluated, and contrasted, with representations and projections that offer meaningful insights? To catalyse the research in AI and the future of cognition, we present the motivation, requirements and possibilities for an atlas of intelligence: an integrated framework and collaborative open repository for collecting and exhibiting information of all kinds of intelligence, including humans, non-human animals, AI systems, hybrids and collectives thereof. After presenting this initiative, we review related efforts and present the requirements of such a framework. We survey existing visualisations and representations, and discuss which criteria of inclusion should be used to configure an atlas of intelligence.

The Elithorn perceptual maze test is widely used in clinical research and practice. However, there is little evidence of its psychometric properties, and its application is limited by the technical difficulty of developing more mazes. The current research aims to adopt a rigorous approach to evaluate 18 mazes that were automatically generated by a novel R software package. Various item response theory models were employed to examine the difficulty parameters. The findings suggested that the data best fitted the Rasch model. The linear logistic test model revealed meaningful contribution to the sources of maze difficulty. Additionally, the linear logistic test model plus error was considered the most parsimonious model. The Automatic Perceptual Maze Test was moderately correlated with a nonverbal intelligence test. By introducing more mazes to provide adequate information on participants’ ability at all levels, the Automatic Perceptual Maze Test promises future clinical and research utility for the study of cognitive performance.

Background: The Center for Epidemiologic Studies Depression Scale (CES-D) is a measure of depressive symptomatology which is widely used internationally. Though previous attempts were made to shorten the CES-D scale, few have attempted to develop a Computerized Adaptive Test (CAT) version for the CES-D. Objective: The aim of this study was to provide evidence on the efficiency and accuracy of the CES-D when administered using CAT using an American sample group. Methods: We obtained a sample of 2060 responses to the CESD-D from US participants using the myPersonality application. The average age of participants was 26 years (range 19-77). We randomly split the sample into two groups to evaluate and validate the psychometric models. We used evaluation group data (n=1018) to assess dimensionality with both confirmatory factor and Mokken analysis. We conducted further psychometric assessments using item response theory (IRT), including assessments of item and scale fit to Samejima’s graded response model (GRM), local dependency and differential item functioning. We subsequently conducted two CAT simulations to evaluate the CES-D CAT using the validation group (n=1042). Results: Initial CFA results indicated a poor fit to the model and Mokken analysis revealed 3 items which did not conform to the same dimension as the rest of the items. We removed the 3 items and fit the remaining 17 items to GRM. We found no evidence of differential item functioning (DIF) between age and gender groups. Estimates of the level of CES-D trait score provided by the simulated CAT algorithm and the original CES-D trait score derived from original scale were correlated highly. The second CAT simulation conducted using real participant data demonstrated higher precision at the higher levels of depression spectrum. Conclusions: Depression assessments using the CES-D CAT can be more accurate and efficient than those made using the fixed-length assessment.