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Objective: This study aims to develop a measurement model for health technology acceptability using a theoretical framework and a range of validated instruments to measure user experience, acceptance, usability, health and digital health literacy. Methods: A cross-sectional evaluation study using a mixed-methods approach was conducted. An online survey was administered to patients who used a pulse oximeter in a virtual hospital setting during COVID-19. The model development was conducted in three steps: (1) exploratory factor analysis for conceptual model development, (2) measurement model confirmation through confirmatory factor analysis followed by structural equation modelling and (3) test of model external validity on four outcome measures. Finally, the different constructs of the developed model were used to compare two types of pulse oximeters by measuring the standardised scores. Results: Two hundred and two participants were included in the analysis, 37.6% were female and the average age was 53 years (SD:15.38). A four-construct model comprising Task Load, Affective Attitude, Self-Efficacy and Value of Use (0.636-0.857 factor loadings) with 12 items resulted from the exploratory factor analysis and yielded a good fit (RMSEA = .026). Health and digital health literacy did not affect the overall reliability of the model. Frustration, performance, trust and satisfaction were identified as outcomes of the model. No significant differences were observed in the acceptability constructs when comparing the two pulse oximeter devices. Conclusions: This article proposes a model for the measurement of the acceptability of health technologies used by patients in a remote care setting based on the use of a pulse oximeter in COVID-19 remote monitoring.

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A thick-lens, structured-light measurement model is introduced to overcome the oversights in traditional models, which often disregard the impact of lens thickness. This oversight can lead to inaccuracies in Scheimpflug camera calculations, causing systematic errors and diminished measurement precision. By geometrical optics, the model treats the camera as a thick lens, factoring in the locations of its principal points and the spatial shifts due to image plane tilting. The model deduces the positional relationship of the thick lens with a tilted optical axis and establishes a linear-structured-light measurement model. Simulations confirm that the model can precisely calculate the 3D coordinates of subjects from image light strip data, markedly reducing systematic errors across the measurement spectrum. Moreover, experimental results suggest that the refined sensor model offers enhanced accuracy and lower standard deviation.

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RATIONALE: Objective Structured Clinical Examinations (OSCEs) are widely used for assessing clinical competence, especially in high-stakes environments such as medical licensure. However, the reuse of OSCE cases across multiple administrations raises concerns about parameter stability, known as item parameter drift (IPD). AIMS & OBJECTIVES: This study aims to investigate IPD in reused OSCE cases while accounting for examiner scoring effects using a Many-facet Rasch Measurement (MFRM) model. METHOD: Data from 12 OSCE cases, reused over seven administrations of the Internationally Educated Nurse Competency Assessment Program (IENCAP), were analyzed using the MFRM model. Each case was treated as an item, and examiner scoring effects were accounted for in the analysis. RESULTS: The results indicated that despite accounting for examiner effects, all cases exhibited some level of IPD, with an average absolute IPD of 0.21 logits. Three cases showed positive directional trends. IPD significantly affected score decisions in 1.19% of estimates, at an invariance violation of 0.58 logits. CONCLUSION: These findings suggest that while OSCE cases demonstrate sufficient stability for reuse, continuous monitoring is essential to ensure the accuracy of score interpretations and decisions. The study provides an objective threshold for detecting concerning levels of IPD and underscores the importance of addressing examiner scoring effects in OSCE assessments. The MFRM model offers a robust framework for tracking and mitigating IPD, contributing to the validity and reliability of OSCEs in evaluating clinical competence.

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INTRODUCTION: The self-medication hypothesis (SMH) suggests that individuals consume alcohol to alleviate stressful emotions. Still, the underlying mechanisms between stress and heavy episodic drinking remain to be explored. Impaired control over drinking (IC) reflects a failure of self-regulation specific to the drinking context, with individuals exceeding self-prescribed limits. Parenting styles experienced during childhood have a lasting influence on the stress response, which may contribute to IC. METHOD: We examined the indirect influences of parenting styles (e.g., permissive, authoritarian, and authoritative) on heavy episodic drinking and alcohol-related problems through the mediating mechanisms of stress and IC. We fit a latent measurement model with 938 (473 men; 465 women) university students, utilizing bootstrap confidence intervals, in Mplus 8.0. RESULTS: Higher levels of authoritative parenting (mother and father) were indirectly linked to fewer alcohol-related problems and less heavy episodic drinking through less stress and IC. Maternal permissiveness was indirectly linked to more alcohol-related problems and heavy episodic drinking through more stress and, in turn, more IC. Impaired control appeared to be a mediator for stress and alcohol-related problems. CONCLUSIONS: Maternal permissiveness contributes to the use of alcohol to alleviate stress. Thus, reducing stress may reduce problematic heavy drinking and alcohol problems among emerging adults with high IC who may also have experienced permissive parenting. Stress may exacerbate behavioral dysregulation of drinking within self-prescribed limits.

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PURPOSE: The aim of this study was to develop the Japanese version of the ABILHAND-Kids and to examine its psychometric properties for Japanese children with cerebral palsy (CP). METHODS: The experimental version of 75 items was developed using forward-backward translation method. Parents of 137 children with CP answered it. Their responses were analyzed to successive items, and psychometric properties of the final version were investigated through the Rasch measurement model. RESULTS: The Japanese version of the ABILHAND-Kids contained 22 items. It showed valid item-patient targeting, no significant floor and ceiling effects, and no differential item functioning for demographic and clinical subgroups. All items contributed to the definition of one-dimensional measure. For internal consistency, the person separation index was 0.94. For test-retest reliability, the intraclass correlation coefficients were 0.96 (95% CI: 0.92-0.98). The minimal detectable difference was calculated with a logit score of 0.79 and a total raw score of 4.50. The logit score showed a strong correlation with the Manual Ability Classification System level (ρ= -0.70) and the Gross Motor Function Classification System level (ρ= -0.62). CONCLUSIONS: The Japanese version of the ABILHAND-Kids was found to be valid and reliable. It appears to be a good tool for assessing manual abilities in daily activities in children with CP.

Impairment of upper limb function affects participation and quality of life in children with cerebral palsy, and adequate assessment tools are essential to appropriate planning and evaluation of interventions.The Japanese version of the ABILHAND-Kids is a valid and reliable measure of manual ability in children with cerebral palsy.The Japan-specific items and calibrations allows for evaluation that takes Japanese culture into consideration.

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The measurement process of ground shock wave overpressure is influenced by complex field conditions, leading to notable errors in peak measurements. This study introduces a novel pressure measurement model that utilizes the Rankine-Hugoniot relation and an equilateral ternary array. The research delves into examining the influence of three key parameters (array size, shock wave incidence angle, and velocity) on the precision of pressure measurement through detailed simulations. The accuracy is compared with that of a dual-sensor array under the same conditions. Static explosion tests were conducted using bare charges of 0.3 kg and 3 kg TNT to verify the numerical simulation results. The findings indicate that the equilateral ternary array shock wave pressure measurement method demonstrates a strong anti-interference capability. It effectively reduces the peak overpressure error measured directly by the shock wave pressure sensor from 17.73% to 1.25% in the test environment. Furthermore, this method allows for velocity-based measurement of shock wave overpressure peaks in all propagation direction, with a maximum measurement error of 3.59% for shock wave overpressure peaks ≤ 9.08 MPa.

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We develop a contextual measurement model (CMM) that is used for the clarification of the quantum foundations. This model matches Bohr's views on the role of experimental contexts. CMM is based on a contextual probability theory that is connected with generalized probability theory. CMM covers measurements in classical, quantum and semi-classical physics. The CMM formalism is illustrated by a few examples. We consider the CMM framing of classical probability, the von Neumann measurement theory and the quantum instrument theory. CMM can also be applied outside of physics, e.g. in cognition, decision-making and psychology, the so-called quantum-like modelling.

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The constructs of motivation (or needs, motives, etc.) to explain higher-order behavior have burgeoned in psychology. In this article, we critically evaluate such high-level motivation constructs that many researchers define as causal determinants of behavior. We identify a fundamental issue with this predominant view of motivation, which we called the black-box problem. Specifically, high-level motivation constructs have been considered as causally instigating a wide range of higher-order behavior, but this does not explain what they actually are or how behavioral tendencies are generated. The black box problem inevitably makes the construct ill-defined and jeopardizes its theoretical status. To address the problem, we discuss the importance of mental computational processes underlying motivated behavior. Critically, from this perspective, motivation is not a unitary construct that causes a wide range of higher-order behavior --- it is an emergent property that people construe through the regularities of subjective experiences and behavior. The proposed perspective opens new avenues for future theoretical development, i.e., the examination of how motivated behavior is realized through mental computational processes.

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Coefficient omega indices are model-based composite reliability estimates that have become increasingly popular. A coefficient omega index estimates how reliably an observed composite score measures a target construct as represented by a factor in a factor-analysis model; as such, the accuracy of omega estimates is likely to depend on correct model specification. The current paper presents a simulation study to investigate the performance of omega-unidimensional (based on the parameters of a one-factor model) and omega-hierarchical (based on a bifactor model) under correct and incorrect model misspecification for high and low reliability composites and different scale lengths. Our results show that coefficient omega estimates are unbiased when calculated from the parameter estimates of a properly specified model. However, omega-unidimensional produced positively biased estimates when the population model was characterized by unmodeled error correlations or multidimensionality, whereas omega-hierarchical was only slightly biased when the population model was either a one-factor model with correlated errors or a higher-order model. These biases were higher when population reliability was lower and increased with scale length. Researchers should carefully evaluate the feasibility of a one-factor model before estimating and reporting omega-unidimensional.

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Measurement invariance of psychological test batteries is an essential quality criterion when the test batteries are administered in different cultural and language contexts. The purpose of this study was to examine to what extent measurement model fit and measurement invariance across the two largest language groups in Switzerland (i.e., German and French speakers) can be assumed for selected general mental ability and personality tests used in the Swiss Armed Forces' cadre selection process. For the model fit and invariance testing, we used Bayesian structural equation modeling (BSEM). Because the sizes of the language group samples were unbalanced, we reran the invariance testing with the subsampling procedure as a robustness check. The results showed that at least partial approximate scalar invariance can be assumed for the constructs. However, comparisons in the full sample and subsamples also showed that certain test items function differently across the language groups. The results are discussed regarding the three following issues: First, we critically discuss the applied criterion and alternative effect size measures for assessing the practical importance of non-invariances. Second, we highlight potential remedies and further testing options, that can be applied, once certain items have been detected to function differently. Third, we discuss alternative modeling and invariance testing approaches to BSEM and outline future research avenues.

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BACKGROUND: We recently developed a novel, preference-based method (Better-Worse, BW) for measuring health status, expressed as a single metric value. We have since expanded it by developing the Drop-Down (DD) method. This article presents a head-to-head comparison of these two methods. We explored user feasibility, interpretability and statistics of the estimated coefficients, and distribution of the computed health-state values. METHODS: We conducted a cross-sectional online survey among patients with various diseases in the USA. The BW and DD methods were applied in the two arms of the study, albeit in reverse order. In both arms, patients first performed a descriptive task (Task 1) to rate their own health status according to the 12 items (each with 4 levels) in the CS-Base health-outcome instrument. They then performed Task 2, in which they expressed preferences for health states by the two methods. We then estimated coefficients for all levels of each item using logistic regression and used these to compute values for health states. RESULTS: Our total sample comprised 1,972 patients. Completion time was < 2 min for both methods. Both methods were scored as easy to perform. All DD coefficients were highly significant from the reference level (P < 0.001). For BW, however, only the second-level coefficient of "Cognition" was significantly different (P = 0.026). All DD coefficients were more precise with narrower confidence intervals than those of the BW method. CONCLUSIONS: Both the BW and DD are novel methods that are easy to apply. The DD method outperformed the BW method in terms of the precision of produced coefficients. Due to its task, it is free from a specific distorting factor that was observed for the BW method.

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Under the influence of multiple types of noises, missing measurement, one-step measurement delay and packet loss, the robust Kalman estimation problem is studied for the multi-sensor descriptor system (MSDS) in this paper. Moreover, the established MSDS model describes uncertain-variance noises, multiplicative noises, time delay and packet loss phenomena. Different types of noises and packet loss make it more difficult to build the estimators of MSDS. Firstly, MSDS is transformed to the new system model by applying the singular value decomposition (SVD) method, augmented state and fictitious noise approach. Furthermore, the robust Kalman estimator is constructed for the newly deduced augmented system based on the min-max robust estimation principle and Kalman filter theory. In addition, the given estimator consists of four parts, which are the usual Kalman filter, predictor, smoother and white noise deconvolution estimator. Then, the robust fusion Kalman estimator is obtained for MSDS according to the relation of augmented state and the original system state. Simultaneously, the robustness is demonstrated for the actual Kalman estimator of MSDS by using the mathematical induction method and Lyapunov's equation. Furthermore, the error variance of the obtained Kalman estimator is guaranteed to the upper bound for all admissible uncertain noise variance. Finally, the simulation example of a circuit system is examined to illustrate the performance and effectiveness of the robust estimators.

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The objective of the study was to specify an abbreviated model of the school coexistence questionnaire for non-violence (CENVI) for students from 5th to 8th grade (9 to 14 years old), in order to determine the perception of violence and management of school coexistence, and the differences between Mapuche and non-Mapuche students. A total of 1870 students from schools in the city of Temuco (Chile) responded to the CENVI questionnaire. There were two samples: (1) Pandemic, with online, face-to-face and hybrid classes; and (2) Post-pandemic, with face-to-face classes. Sample 1 consisted of 848 students aged 9 to 15 years (M = 11.90; SD = 1.27). Sample 2 consisted of 1022 students aged 9 to 14 years (M = 11.46; SD = 1.14). The questionnaire was validated using expert inter-judgment and Confirmatory Factor Analysis. A good fit of the proposed model to the data and good internal consistency measured according to the composite reliability were found, and convergent validity was demonstrated. Mapuche students perceived more physical violence and social exclusion. Cut-off points were proposed for the interpretation of the results. In the data, the effect of Coexistence Management on School Violence was null. The discussion approaches the findings from the literature on education in spaces of socio-cultural diversity in a Mapuche context.

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The interpretation of X-ray photoelectron spectroscopy (XPS) data relies on measurement models that depend on several parameters, including the photoelectron attenuation length and X-ray photon flux. However, some of these parameters are not known, because they are not or cannot be measured. The unknown geometrical parameters can be lumped together in a multiplicative factor, the alignment parameter. This parameter characterizes the ability of the exciting light to interact with the sample. Unfortunately, the absolute value of the alignment parameter cannot be measured directly, in part because it depends on the measurement model. Instead, a proxy for the experimental alignment is often estimated, which is closely related to the alignment parameter. Here, a method for estimating the absolute value of the alignment parameter based on the raw XPS spectra (i.e. non-processed photoelectron counts), the geometry of the sample and the photoelectron attenuation length is presented. The proposed parameter estimation method enables the quantitative analysis of XPS spectra using a simplified measurement model. All computations can be executed within the open and free Julia language framework PROPHESY. To demonstrate feasibility, the alignment parameter estimation method is first tested on simulated data with known acquisition parameters. The method is then applied to experimental XPS data and a strong correlation between the estimated alignment parameter and the typically used alignment proxy is shown.

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The position and altitude of a sub-reflector have an important influence on the pointing accuracy of a radio telescope. With the increase of the antenna aperture, the stiffness of the support structure for the sub-reflector decreases. This causes deformation of the support structure when environmental loads, such as gravity, temperature, and wind load, are applied to the sub-reflector, which will seriously influence antenna pointing accuracy. This paper proposes an online measurement and calibration method for assessing the deformation of the sub-reflector support structure based on the Fiber Bragg Grating (FBG) sensors. Firstly, a reconstruction model between the strain measurements and the deformation displacements of a sub-reflector support structure is established based on the inverse finite element method (iFEM). In addition, a temperature-compensating device with an FBG sensor is designed to eliminate the effects of temperature variations on strain measurements. Considering the lack of the trained original correction, a non-uniform rational B spline (NURBS) curve is built to extend the sample data set. Next, a self-structuring fuzzy network (SSFN) is designed for calibrating the reconstruction model, which can further improve the displacement reconstruction accuracy of the support structure. Finally, a full-day experiment was carried out using a sub-reflector support model to verify the effectiveness of the proposed method.

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Selenium (Se) is an essential nutrient for humans and enters our food chain through bioavailable Se in soil. Atmospheric deposition is a major source of Se to soils, driving the need to investigate the sources and sinks of atmospheric Se. Here, we used Se concentrations from PM2.5 data at 82 sites from 1988 to 2010 from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network in the US to identify the sources and sinks of particulate Se. We identified 6 distinct seasonal profiles of atmospheric Se, grouped by geographical location: West, Southwest, Midwest, Southeast, Northeast, and North Northeast. Across most of the regions, coal combustion is the largest Se source, with a terrestrial source dominating in the West. We also found evidence for gas-to-particle partitioning in the wintertime in the Northeast. Wet deposition is an important sink of particulate Se, as determined by Se/PM2.5 ratios. The Se concentrations from the IMPROVE network compare well to modeled output from a global chemistry-climate model, SOCOL-AER, except in the Southeast US. Our analysis constrains the sources and sinks of atmospheric Se, thereby improving the predictions of Se distribution under climate change.

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Current machine learning (ML) applications in atmospheric science focus on forecasting and bias correction for numerical modeling estimations, but few studies examined the nonlinear response of their predictions to precursor emissions. This study uses ground-level maximum daily 8-hour ozone average (MDA8 O3) as an example to examine O3 responses to local anthropogenic NOx and VOC emissions in Taiwan by Response Surface Modeling (RSM). Three different datasets for RSM were examined, including the Community Multiscale Air Quality (CMAQ) model data, ML-measurement-model fusion (ML-MMF) data, and ML data, which respectively represent direct numerical model predictions, numerical predictions adjusted by observations and other auxiliary data, and ML predictions based on observations and other auxiliary data. The results show that both ML-MMF (r = 0.93-0.94) and ML predictions (r = 0.89-0.94) present significantly improved performance in the benchmark case compared with CMAQ predictions (r = 0.41-0.80). While ML-MMF isopleths exhibit O3 nonlinearity close to actual responses due to their numerical base and observation-based correction, ML isopleths present biased predictions concerning their different controlled ranges of O3 and distorted O3 responses to NOx and VOC emission ratios compared with ML-MMF isopleths, which implies that using data without support from CMAQ modeling to predict the air quality could mislead the controlled targets and future trends. Meanwhile, the observation-corrected ML-MMF isopleths also emphasize the impact of transboundary pollution from mainland China on the regional O3 sensitivity to local NOx and VOC emissions, which transboundary NOx would make all air quality regions in April more sensitive to local VOC emissions and limit the potential effort by reducing local emissions. Future ML applications in atmospheric science like forecasting or bias correction should provide interpretability and explainability, except for meeting statistical performance and providing variable importance. Assessment with interpretable physical and chemical mechanisms and constructing a statistically robust ML model should be equally important.

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The present study was conducted to examine the psychometric properties and gender invariance of the Iranian version of the Dimensions of Identity Development Scale (DIDS). A total of 1453 adolescents (50.8% female; 14-18 years old, mean = 15.48) participated in a cross-sectional study and completed the DIDS and the Youth Self-Report of behavior problems. The Confirmatory Factor Analysis supported the six-factor model of the DIDS, echoing past studies showing the original 5th factor (Exploration in Depth) being divided into Exploration in Depth and Reconsidering the Commitment. The invariance testing showed comparable measurement properties of the DIDS across males and females (strict measurement invariance). Further, behavior problems were associated positively with Ruminative Exploration and negatively with Commitment Making, Identification with Commitments, Exploration in Depth, and Reconsideration of Commitments, whereas the opposite was true for academic performance. A six-factor DIDS was shown to be a valid and reliable measure for the assessment of identity development dimensions among Iranian adolescents. Future studies in the Iranian context evaluating the identity clusters derived from identity dimensions and their gender differences are warranted.

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Percentage of uncontaminated correlations (PUC), explained common variance (ECV), and omega hierarchical (ωH) have been used to assess the degree to which a scale is essentially unidimensional and to predict structural coefficient bias when a unidimensional measurement model is fit to multidimensional data. The usefulness of these indices has been investigated in the context of bifactor models with balanced structures. This study extends the examination by focusing on bifactor models with unbalanced structures. The maximum and minimum PUC values given the total number of items and factors were derived. The usefulness of PUC, ECV, and ωH in predicting structural coefficient bias was examined under a variety of structural regression models with bifactor measurement components. Results indicated that the performance of these indices in predicting structural coefficient bias depended on whether the bifactor measurement model had a balanced or unbalanced structure. PUC failed to predict structural coefficient bias when the bifactor model had an unbalanced structure. ECV performed reasonably well, but worse than ωH.

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BACKGROUND: The Occupational Self-Assessment (OSA) is a self-report measure of occupational competence and values used to identify goals and assess outcomes. The Icelandic version of the OSA (OSA-IS) has been through several developmental stages to evaluate its psychometric properties. Through each stage, revisions have been made. AIM: To investigate the psychometric properties of the fourth revised version of OSA-IS. MATERIALS AND METHODS: Retrospective data from 291 rehabilitation clients with a range of conditions were analysed using Rasch analysis of unidimensionality and descriptive statistics. All statistics were compared to established criteria. RESULTS: Analyses suggest the OSA-IS items define unidimensional constructs of occupational competence and values. Most (89%) participants completed the assessment in a reliable manner and no association was observed between demographic variables and fit status. Differences in the item hierarchies were observed between the original OSA and the OSA-IS, suggesting that Icelandic clients responded differently due to cultural, linguistic and/or sample differences. CONCLUSION AND SIGNIFICANCE: OSA-IS is a psychometrically sound instrument that may be used to support identification of client-centred goals and for intervention development. Clinicians should use score tables specifically developed for the OSA-IS to measure outcomes.

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