Best-fit Model Research Articles

Development and psychometric testing of the nurses' core competencies for public health emergencies scale: A multicenter cross-sectional study

BackgroundThe significance of addressing public health emergencies (PHEs) emphasizes the pivotal role of a skilled nursing workforce in effective preparedness and response. However, there's a lack of comprehensive assessments tailored to nurses' core competencies, serving as a standard for ongoing education in preparedness. ObjectiveThis study aimed to develop a psychometrically sound scale for identifying and measuring essential core competencies crucial for nurses during public health emergencies. DesignA multicenter cross-sectional study was conducted. SettingsTwelve tertiary hospitals in Shanghai. ParticipantsAffiliated nurses with prior experience and significant exposure to public health emergencies were recruited. MethodsThe study underwent development and validation in two stages, utilizing qualitative data analysis, a Delphi expert panel, and an empirical quantitative cross-sectional survey. The Nurses' Core Competencies for Public Health Emergencies (NCC-PHEs) scale and a demographic questionnaire were distributed between January 2023 and March 2023. Exploratory factor analysis, confirmatory factor analysis, Pearson correlations, and Cronbach's alpha analyses were employed to identify theoretical constructs, assess scale reliability, and establish validity. ResultsAnalysis was conducted on valid data collected from 1481 nurses. Factor analysis identified 47 professional nursing competencies arranged within a four-factor high-order model: prevention competencies (9 items), preparation competencies (7 items), response competencies (comprising basic and advanced levels) (26 items), and recovery competencies (5 items). Structural equation modelling confirmed satisfactory factor loadings and a good model fit, validating construct integrity. The reliability of the total scale was confirmed. ConclusionThis study presents a valid scale that empirically measures nurses' core competencies crucial for preparedness and response during public health emergencies. The findings offer instrumental support for guiding the development of future courses and training programs in nursing research and practice.

Psychometric Evaluation of the Brief-COPE Inventory and Exploration of Factors Associated with Perceived Stress among Peruvian Nurses.

This study aimed to analyze the psychometric properties of the Brief-COPE Inventory and to determine its concurrent validity by examining its association with perceived stress among Peruvian nurses. A psychometric study was conducted with 434 Peruvian nurses to evaluate the psychometric properties of the Brief-COPE Inventory through confirmatory factor analysis. Three stepwise variable selection regression models were implemented. The three-factor model of the Brief-COPE Inventory demonstrated adequate fit indices (root mean square error of approximation = 0.052, standardized root mean square residual = 0.068, and both the comparative fit index and the Tucker-Lewis index = 0.95). Additionally, the factors were significantly correlated (p < 0.001), and the reliability was adequate (ω = 0.90). Nurses reported a medium level of perceived stress, with associated factors including having received stress management training, fear of COVID-19, and problem-focused coping strategies (p < 0.05). This study confirms that the Brief-COPE Inventory is a valid tool for measuring coping strategies among Peruvian nurses due to its good model fit, excellent reliability, and concurrent validity with perceived stress. However, further research is needed to assess its validity in the specific areas of performance perceived by nursing professionals.

End-users’ acceptance of ’X as a Service’: Evidence from agriculture 4.0

With the rapid advancement of information technology, a new service model known as X as a Service has emerged. X can represent diverse resources such as platforms, infrastructure, farming, mobility, security, and more, allowing users to meet their needs flexibly and cost-effectively. The successful application of X as a Service heavily depends on end-users’ acceptance. This study explored the motivation factors for X as a Service based on the evidence from adopting Farming as a Service in Agriculture 4.0 among farmers in Northeastern China. We provided a theoretical framework for Farming as a Service adoption behavior, covering factors including personalization, perceived enjoyment, functionality, perceived risk, financial consequences, and perceived network externality. The effectiveness of the research model was assessed and validated through a two-stage procedural approach, utilizing partial least squares structural equation modeling. Results revealed that our proposed acceptance model for Farming as a Service exhibited a good model fit, accounting for 84.4 % of the variance in adoption intentions. Research findings highlighted that perceived network externality and functionality were the most influential factors in determining users’ adoption intentions for Farming as a Service. Conversely, perceived risk emerged as a significant negative factor influencing adoption. Furthermore, financial consequences, perceived enjoyment, and personalization also played crucial roles as determinants of user adoption. These findings offered valuable insights for service providers to improve their products, services, and marketing strategies.

Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.

Small clinics are important in providing health care in local communities. Accurately predicting their closure would help manage health care resource allocation. There have been few studies on the prediction of clinic closure using machine learning techniques. This study aims to test the feasibility of predicting the closure of medical and dental clinics (MCs and DCs, respectively) and investigate important factors associated with their closure using machine running techniques. The units of analysis were MCs and DCs. This study used health insurance administrative data. The participants of this study ran and closed clinics between January 1, 2020, and December 31, 2021. Using all closed clinics, closed and run clinics were selected at a ratio of 1:2 based on the locality of study participants using the propensity matching score of logistic regression. This study used 23 and 19 variables to predict the closure of MCs and DCs, respectively. Key variables were extracted using permutation importance and the sequential feature selection technique. Finally, this study used 5 and 6 variables of MCs and DCs, respectively, for model learning. Furthermore, four machine learning techniques were used: (1) logistic regression, (2) support vector machine, (3) random forest (RF), and (4) Extreme Gradient Boost. This study evaluated the modeling accuracy using the area under curve (AUC) method and presented important factors critically affecting closures. This study used SAS (version 9.4; SAS Institute Inc) and Python (version 3.7.9; Python Software Foundation). The best-fit model for the closure of MCs with cross-validation was the support vector machine (AUC 0.762, 95% CI 0.746-0.777; P<.001) followed by RF (AUC 0.736, 95% CI 0.720-0.752; P<.001). The best-fit model for DCs was Extreme Gradient Boost (AUC 0.700, 95% CI 0.675-0.725; P<.001) followed by RF (AUC 0.687, 95% CI 0.661-0.712; P<.001). The most significant factor associated with the closure of MCs was years of operation, followed by population growth, population, and percentage of medical specialties. In contrast, the main factor affecting the closure of DCs was the number of patients, followed by annual variation in the number of patients, year of operation, and percentage of dental specialists. This study showed that machine running methods are useful tools for predicting the closure of small medical facilities with a moderate level of accuracy. Essential factors affecting medical facility closure also differed between MCs and DCs. Developing good models would prevent unnecessary medical facility closures at the national level.

The consequence of higher-order curvature-based constraints on [formula omitted] gravity

In this investigation, we perform an observational statistical analysis in the theory of f(R,Lm) gravity. The proposed theoretical model is based on the Ricci scalar’s non-linear contribution. We use a distinct parametrization for the deceleration parameter and constrain the model parameters by using various observational data. To determine the best-fit model for the cosmological parameters, we use different observational datasets such as the Hubble Space Telescope, the Pantheon Supernova Survey, the Gold dataset, the Gamma-Ray Burst (GRB), and the Baryon Acoustic Oscillations (BAO). Furthermore, we study the late-time cosmic evolution of the Universe in detail and examine the implications of the constraint values on cosmological parameters. Additionally, we conduct a thorough comparison with the standard cosmological model ΛCDM and other standard models obtained by Odintsov et al. (2024); Odintsov et al. (2023) to examine the validity of our proposed model in the low-redshift regimes. Finally, we find that the proposed model encapsulates an intriguing transition from early deceleration at high redshift to acceleration at low redshift, a quintessence dark energy scenario, and convergence towards the well-established ΛCDM model in late-time Universe’s evolution.

Hybrid nanocellulose material as an adsorbent to remove reactive yellow 2 dye

Textile dyes are frequently disposable in aqueous effluents, making it difficult to remove them from industrial effluents before their release to natural waters. This paper deals with the fabrication of cellulose-based adsorbents by reacting nanocelulose crystalline (nanocel) with N-[3-(trimethoxysilyl)propyl]ethylenediamine (TMSPEDA), forming the hybrid (silylpropyl)ethylenediamine@nanocellulose (SPEDA@nanocel), which was employed as adsorbent for the uptake of reactive yellow 2 dye (RY-2) from aqueous effluents. Characterisation of SPEDA@nanocel was carried out using FTIR, SEM–EDS, XRD, TGA, surface area, pHpzc, and hydrophobicity/hydrophilicity ratio (HI). Also, adsorption studies were thoroughly investigated. The effect of initial pH indicated that the maximum uptake of RY-2 takes place at pH 2, which is an indication of the electrostatic mechanism. The kinetic data carried out with 250 and 500 mg L−1 RY-2 with SPEDA@nanocel followed better the nonlinear fractional-like pseudo-first-order model. The t0.5 and t0.95 for the dye uptake were about 30 and 141 min, respectively. The equilibrium data from 10 to 45 °C indicated that the Liu isotherm model was the best-fitted isothermal model. The maximum sorption capacity attained was 112.3 mg g−1 at 45 °C. The thermodynamic data have shown that the equilibrium was favorable and endothermic, and the ΔH° was compatible with an electrostatic attraction between RY-2 and SPEDA@nanocel. Experiments of desorption of loaded adsorbent showed promising results for real applications since at least 5 adsorption/desorption cycles could be employed without significant changes in the recovery and with high precision.

The relationship between socioeconomic status, medical accessibility, hope and psychological resilience of caregivers of children with chronic kidney disease in China: structural equation model

BackgroundChronic kidney disease (CKD) is the third most common cause of death after cancer and heart disease. The continuous treatment of children with CKD was greatly challenged during the COVID-19 pandemic, which significantly impacted the CKD children’s prognosis and the caregivers’ psychological status. However, the influence mechanism of socioeconomic status, medical delay duration, traffic pressure, and online consultation duration on caregivers’ hope and psychological resilience still lacks relevant evidence.MethodsThis study investigated the general social information, hope, and psychological resilience of 247 caregivers with CKD in 13 provinces of China in March 2020. Factor analysis and an exploratory Structural Equation Model ( SEM ) were used to find the best-fit model, and Bootstrapping was used to calculate the 95% CI of indirect effects.ResultsThe factor analysis obtained four common factors, namely, socioeconomic status (annual family income, education, and career stability), medical accessibility (online consultation duration, medical delay duration, and traffic pressure), hope (positive attitude, positive action, and intimate relationship) and psychological resilience (optimism, tenacity, and strength), with the cumulative contribution rate of 65.34%. Bootstrapping obtains the socioeconomic status β = 0.30 (95% CI [0.14, 0.47], P = 0.002), medical accessibility β = 0.31 (95% CI [0.12, 0.47], P = 0.002), and hope β = 0.40 (95% CI [0.27, 0.52], P = 0.002) has a direct impact on psychological resilience of CKD children caregivers, followed by medical accessibility β = 0.20 (95% CI [0.10, 0.38], P = 0.001) and hope β = 0.23 (95% CI [0.16, 0.32], P = 0.001) plays a mediating role between socioeconomic status and psychological resilience. The indirect impact effect β = 0.35 (95% CI [0.25, 0.50], P = 0.001) is greater than the direct impact effect β = 0.30 (95% CI [0.14, 0.47], P = 0.002).ConclusionsSufficient attention should still be given to children with immunodeficiency after the COVID-19 pandemic, such as CKD, to avoid infection of deadly. Secondly, the government should vigorously develop Primary medical institutions to ensure efficient treatment of severe patients in tertiary hospitals; Finally, the professional literacy of medical workers in remote diagnosis and treatment should be improved to enhance the country’s emergency response capacity for similar major public events and the requirements for normalised epidemic prevention and control.

Psychometric assessment of the Persian translation of the School Counselor Noncognitive Skills Scale using Iranian high-school students

Assessing students’ perceptions of the services provided by school counselors is crucial in promoting these services. However, there is no reliable scale for this assessment in the Persian language to date. Thus, this study aimed to translate and validate the School Counselor Noncognitive Skills Scale (SCNSS) among Iranian high school students. The English version of the SCNSS was translated into Persian, and its content and face validity were confirmed through cognitive interviews and expert evaluations. Using a convenience sampling method, data were collected from 308 high school students across three educational districts in Sanandaj, Iran. Confirmatory factor analysis supported the three-factor structure of the SCNSS, demonstrating good model fit. Measurement invariance analysis indicated consistency across genders. The internal consistency of the scale was high, with Cronbach’s alpha coefficients exceeding .86 for all subscales. Convergent validity was established through significant correlations with the School Belonging Scale (SBS). Additionally, a positive correlation was found between the number of counseling sessions attended and students’ perceptions of their counselors’ noncognitive skills. These findings suggest that the Persian version of the SCNSS is a valid and reliable tool for assessing the impact of school counselors on the development of noncognitive skills among Iranian students.

Harmonizing perspectives to understand attitudes: A mixed methods approach to crafting an assessment literacy attitude scale

Assessment literacy's vital role in faculty effectiveness within higher education lacks sufficient tools for measuring faculty attitudes on this matter. Employing a sequential mixed-methods approach, this study utilized the theory of planned behavior to develop the Assessment Literacy Attitude Scale (ALAS) and evaluate its psychometric properties within the U.S. higher education context. The qualitative phase involved a literature review of relevant studies and existing self-report measures, interviews with stakeholders, and panel reviews to shape initial item development. Following the establishment of a conceptual foundation and a comprehensive overview of the scale's construction, our study advanced to the quantitative stage that involves factor analytical and item response theory approaches using data from 260 faculty across three public universities in the U.S. Exploratory factor analysis (EFA) was employed initially to obtain preliminary insights into the scale's factorial structure and dimensionality. Confirmatory factor analysis (CFA) was subsequently applied with separate data and the findings largely supported the conclusions from the EFA. Exploratory and confirmatory factor analyses resulted in 15 items loading across two factors in a good model fit range. Finally, we used nonparametric item response theory (IRT) techniques based on Mokken Scale Analysis (MSA) to evaluate individual items for evidence of effective psychometric properties to support the interpretation of ALAS scores, including monotonicity, scalability, and invariant item ordering. The newly-developed scale shows promise in assessing faculty attitudes toward enhancing their assessment literacy.

Exploring the Complex Ionization Environment of the Turbulent DM Tau Disk

Ionization drives important chemical and dynamical processes within protoplanetary disks, including the formation of organics and water in the cold midplane and the transportation of material via accretion and magnetohydrodynamic flows. Understanding these ionization-driven processes is crucial for understanding disk evolution and planet formation. We use new and archival Atacama Large Millimeter/submillimeter Array observations of HCO+, H13CO+, and N2H+ to produce the first forward-modeled 2D ionization constraints for the DM Tau protoplanetary disk. We include ionization from multiple sources and explore the disk chemistry under a range of ionizing conditions. Abundances from our 2D chemical models are postprocessed using non-LTE radiative transfer, visibility sampling, and imaging, and are compared directly to the observed radial emission profiles. The observations are best fit by a modestly reduced cosmic-ray ionization rate (ζ CR ∼10−18 s−1) and a hard X-ray spectrum (hardness ratio = 0.3), which we associate with stellar flaring conditions. Our best-fit model underproduces emission in the inner disk, suggesting that there may be an additional mechanism enhancing ionization in DM Tau’s inner disk. Overall, our findings highlight the complexity of ionization in protoplanetary disks and the need for high-resolution multiline studies.

Data-driven analysis of heating and cooling base temperatures for buildings: Case studies in South Korea

To investigate statistical differences in the heating (Tbh) and cooling base temperatures (Tbc) across various building types, vintages, and sizes, we conducted case studies in Seoul, South Korea, based on data from more than 10,000 buildings. To assess the relative impacts of internal and external loads on Tbh and Tbc, we used a core-to-perimeter zone ratio approximated by the average floor area, defined as the floor area per building floor. Employing the energy signature method, change-point regression, and calendarization, we determined the best-fit change-point model and the corresponding Tbh and Tbc for each building. Our findings revealed statistically significant differences in Tbhs and Tbcs, with variations of up to 6 and 4 °C, respectively, across six building types. Notably, for most building types, Tbh and Tbc decreased as the average floor area increased, possibly influenced by a higher core zone to total area ratio. This highlights the predominant impact of internal loads over external loads. The influence of the average floor area on Tbh and Tbc outweighed that of building vintage, particularly for lodging, medical, and sales facilities, where Tbh and Tbc decreased in the more recent vintage buildings. However, this influence was less pronounced for facilities for senior citizens and children and education and research facilities, possibly due to factors such as diverse building shapes and the thermal preferences of occupants. Although this study focuses on South Korea, our data-driven approach provides statistical evidence for refining base temperature and improving traditional degree-day methods. Our findings suggest the need to establish base temperatures that consider various building types, vintages, and average floor areas, especially for large metropolitan cities where these factors vary widely.

Adaption and validation of a quantitative measure for associational social capital among Chinese parents in Hong Kong

ABSTRACT This study investigates the reliability and validity of the Associational Social Capital Scale (ASCS) among the parent population. Participants were Chinese parents (N = 577) residing in Hong Kong. Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to explore the factor structure of ASCS. EFA yielded a new threefold factor structure (i.e. social cohesion and relationship, common value, and position) with 10 items, which was supported by the CFA and explained 75.469% of the total variance. The CFA findings endorsed that the goodness-of-fit indices were statistically acceptable, indicating a good fitting model. The internal consistency reliability and construct validity of the scale were also examined. Considering this evidence, we concluded that the new version of ASCS is a valid and reliable measure for assessing parental associational social capital. The results also shed light on parenting interventions in the social work field.

Development and preliminary validation of a questionnaire on the care needs of family carers of older individuals with disabilities in China: a mixed methods study

BackgroundEnsuring the wellness of older individuals with disabilities requires prioritising the care needs of their carers. However, current practice lacks validated tools to measure the needs of carers in home environments. Thus, this study aimed to create and test a questionnaire on the care needs of family carers of older people with disabilities in China.MethodsWe used a standard development process to generate the questionnaire. The pilot testing included cognitive interviews to ensure interpretation as intended. Furthermore, we used a cross-sectional study method to conveniently select 640 Chinese family carers of older people with disabilities from August 1, 2022, to June 11, 2023, for face-to-face investigation. Exploratory factor analysis (EFA) aided in project reduction and factor estimation, with 30 participants undergoing retest evaluations every two weeks. Confirmatory factor analysis (CFA) assessed the model’s structural validity, while internal consistency and retest reliability validated its accuracy.ResultsThese tests established the model: content validity, item analysis and EFA. Six factors extracted from the initial analysis explained 62.891% of the observation variance. CFA showed good model fit, and the questionnaire had good reliability and validity. The final questionnaire included 21 items focusing on six dimensions: care assistance (three items), care environment (three items), care information (three items), formal support (four items), care ability (six items) and self-development (two items).ConclusionThe care needs questionnaire effectively evaluates the needs of family carers in their caring activities.

Individual differences in the forms of personality trait trajectories.

Changes in personality are often modeled linearly or curvilinearly. It is a simplifying-yet untested-assumption that the chosen sample-level model form accurately depicts all person-level trajectories within the sample. Given the complexity of personality development, it seems unlikely that imposing a single model form across all individuals is appropriate. Although typical growth models can estimate individual trajectories that deviate from the average via random effects, they do not explicitly test whether people differ in the forms of their trajectories. This heterogeneity is valuable to uncover, though, as it may imply that different processes are driving change. The present study uses data from four longitudinal data sets (N = 26,469; Mage = 47.55) to empirically test the degree that people vary in best-fitting model forms for their Big Five personality development. Across data sets, there was substantial heterogeneity in best-fitting forms. Moreover, the type of form someone had was directly associated with their net and total amount of change across time, and these changes were substantially misquantified when a worse-fitting form was used. Variables such as gender, age, trait levels, and number of waves were also associated with people's types of forms. Lastly, comparisons of best-fitting forms from individual- and sample-level models indicated that consequential discrepancies arise from different levels of analysis (i.e., individual vs. sample) and alternative modeling choices (e.g., choice of time metric). Our findings highlight the importance of these individual differences for understanding personality change processes and suggest that a flexible, person-level approach to understanding personality development is necessary. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Assessment of Precipitation and Droughts in the Aegean Region Using Stochastic Time Series and Standardized Precipitation Index

This study analyzes drought conditions in the Aegean region using monthly precipitation data from nine stations between 1972 and 2020. The Standardized Precipitation Index (SPI) was calculated for 1-, 3-, 6-, 9-, and 12-month periods to evaluate drought conditions at different timescales and station-specific conditions. The results indicate that short-term droughts are more frequent but shorter in duration, while longer periods exhibit fewer but more prolonged droughts. The relative frequency of drought across all periods ranges between 9% and 27%. The İzmir and Denizli stations were highlighted due to their representation of coastal and inner regions, respectively. The findings show that coastal stations, like İzmir, experience more frequent wet years compared to inner stations like Denizli, which have more dry years. Time series linear autoregressive (AR) models, using SPI-12 data, were developed to represent long-term drought trends and forecasts. The best-fitting models were determined using AIC, AICC, FPE, and Var(e) criteria, with AR(2) generally being the most suitable, except for Denizli. This integrated analysis of SPI and AR models provides a robust basis for understanding regional precipitation regimes and predicting future droughts, aiding in the development of effective drought mitigation strategies and water resource management. Future research is anticipated to extend this analysis to encompass all of Turkey and explore various time series models’ applicability.

Model of Teachers’ Performance in Public Schools of Panabo City, Davao del Norte, Philippines: Teacher's Performance Triad- Exploring the Nexus of Job Design, Commitment, and Satisfaction

In the realm of educational management, teachers' performance is a crucial component in fulfilling the school’s objectives, as it directly influences the quality of education provided to students. Performing teachers contribute significantly to achieving the institution’s goals by ensuring students receive a high-quality education and reach their full potential. This study aimed to develop a best-fit model of job design characteristics, organizational commitment, and job satisfaction in line with teachers’ performance at the Schools Division of Panabo City. It examined the variables that best predict teachers’ performance and determined the best fit structural model. It utilized descriptive, correlational, and causal-comparative research design. This study is significant to Human Resource Management considering that findings were good inputs on developing a framework in improving teacher’s performance. This framework would also be appropriate tool in identifying intervention programs like training, seminar-workshops and other related activities in line with human resource development program. It involved 490 elementary school teachers and utilized 4 sets of modified questionnaires on job design characteristics, organizational commitment, job satisfaction, and teacher performance, which were subjected to validity and reliability. The findings indicated a positive relationship between job design characteristics, and job satisfaction and are predictors of teacher performance with the equation TP = 5.810 + 0.155 (JDC-M) + 0.168 (JS-TS). Moreover, the result concluded that the best fit structural model on teacher’s performance is greatly anchored on these variables: job satisfaction and job design characteristics. It is recommended to have a policy framework in identifying the appropriate intervention programs in job satisfaction and job design characteristics to improve teacher’s performance of Panabo City Division. Additionally, this recommends that the HRMO, Regional, Division and school Administrators as the implementing unit must have the capacity to identify appropriate tasks and offer technical assistance to the teachers of the school that falls under the jurisdiction of the department to organize informative workshops and seminars aimed at boosting the teachers' motivation and effectively addressing their concerns, particularly regarding promotions and benefits. Further, it needs to have a depth exploration of identifying factors on job satisfaction and job design characteristics for the improvement of the performance of teachers.

Dimensionality of instructional quality in physical education. Obtaining students' perceptions using bifactor exploratory structural equation modeling and multilevel confirmatory factor analysis.

In research on instructional quality, the generic model of the three basic dimensions is an established framework, which postulates that the three dimensions of classroom management, student support and cognitive activation represent quality characteristics of instruction that can be generalized across subjects. However, there are hardly any studies that examine if the three basic dimensions model could represent a suitable approach to measure instructional quality in physical education. Based on an extended model of the basic dimensions, a measurement model of instructional quality for physical education is presented, which integrates different theoretical approaches from the fields of educational and psychological research as well as different subfields of sports science in order to test the factorial structure of the corresponding measurement model. 1,047 students from 72 seventh to ninth grade classes from different German-speaking Swiss cantons participated in the study. The conceptualization of the instrument is based on a hybrid approach that integrates generic and subject-specific characteristics. The simultaneous analysis at the individual and class level using MCFA was supplemented by more complex methodological techniques within the relatively new B-ESEM framework at the individual level. The postulated five-factor structure was initially tested using ICM-CFA and showed a good model fit (e.g., χ2/df = 2.32, RMSEA = 0.03, CFI = 0.97, TLI = 0.97, SRMR = 0.04). MCFA revealed a differential factorial structure at both levels of analysis with five factors at the individual level and four factors at the class level (e.g., χ2/df = 2.23, RMSEA = 0.03, CFI = 0.96, TLI = 0.96, SRMR within = 0.04, SRMR between = 0.10). ESEM and B-ESEM outperformed the ICM-CFA and showed an excellent model fit (B-ESEM: χ2/df = 1.19, RMSEA = 0.01, CFI = 1.00, TLI = 1.00, SRMR = 0.01). Inter-factor correlations and factor loadings are largely in line with expectations, indicating arguments for construct validity. The study represents a substantial contribution in linking physical education and the generic research on instructional quality. Overall, strong arguments for the factorial structure of the measurement model were demonstrated. The study can be interpreted as a first step in a multi-step procedure in terms of further validity arguments.

Characterizing social communication among minimally verbal children with autism: An application of item response theory.

Minimally verbal children constitute a portion of the autism spectrum. The paucity of proper measurement tools that sensitively and accurately assess behaviors has been one limiting factor in the improved knowledge of these children. Short of creating and validating a new measurement tool for this subpopulation, this study took an alternative and more immediate approach: conduct a secondary data analysis and examine an existing social communication measure, the Early Social Communication Scales (ESCS), with item response theory. The final sample consisted of 453 minimally verbal children culled from four different completed studies. The IRT models analyzed the frequency of social communication gestures from the ESCS and returned an objective difficulty hierarchy regarding initiations of joint attention and behavior regulation gestures. The best-fitting and final model was a zero-inflated negative binomial model (ZINBM), which determined that joint attention gestures were, on average, more difficult than behavior regulation gestures. Joint attentional shows and gives were essentially absent in the children's repertoire, and behavior regulation reaches were the easiest gestures for this sample. The ZINBM separately modeled children with some gestures and children who did not present with any gestures and determined that behavior regulation reaches and gives were likely the first gestures a child will eventually exhibit among children with no gestures. Methodological contributions and potential future applications of IRT are discussed.

Adapting to AI: exploring the implications of AI integration in shaping the accounting and auditing profession for developing economies

The study investigates the adaptability of the accounting and auditing profession in the face of rapid AI integration, focusing on developing economies with Ghana as a case study. It employs a quantitative approach, using a cross-sectional survey design to collect data from accounting and auditing professionals across various sectors in Ghana. A stratified random sampling technique ensures diverse representation, and the data is analysed using structural equation modelling (SEM) tools. The research identifies six key domains and twelve adaptability attributes, including forces of change, leadership qualities, readiness, approach, and resistance to change, which are critical for the profession’s response to disruptive AI technologies. The extracted model, based on these attributes, was validated through a survey, revealing a good model fit with the Standardized Root Mean Square Residual (SRMR) below 0.08 and a Normed Fit Index (NFI) within an acceptable range. The findings suggest that to remain relevant in the AI era, accounting professionals in developing economies must continuously upskill and reskill, alongside cultivating strong business acumen, interpersonal skills, critical thinking, value-added services, and communication abilities. The study emphasizes the importance of policymakers, professional bodies, and leadership within the profession aligning their strategies with these adaptability attributes to enhance resilience and competitiveness in an AI-driven landscape. This research significantly contributes to the understanding of how the accounting and auditing profession in developing economies can navigate the challenges and opportunities presented by AI.

Psychometric properties of the toddler feeding style questionnaire in fathers and its gender invariance

Parental feeding practices and behaviours are closely related to a number of child outcomes, including eating behaviour and health. The Toddler Feeding Style Questionnaire (TFSQ) was developed to provide an accurate instrument for evaluating parental feeding practices for toddlers by assessing two dimensions: responsiveness and demandingness. As the initial validation study was conducted in a sample of mothers only, the aims of this study are: a) to validate the TFSQ in fathers, b) to analyse its measurement invariance as a function of gender and parenting experience and c) to explore associations between socio-demographic variables and both dimensions. The TFSQ was completed by 377 fathers and 583 mothers. CFA confirmed the two-factor structure within the same items as the initial validation study, with good model fit indices (X2 = 230.089; TLI = 0.883; CFI = 0.91; RMSEA = 0.08 (90% CI 0.068–0.092), SRMR = 0.070). Strict invariance measurement in terms of gender and parenting experience was achieved. Overall, fathers scored higher than mothers on demandingness, regardless of their parenting experience. Primiparous mothers scored higher than multiparous mothers and primiparous fathers on the responsiveness dimension. However, there were no gender-based differences on this dimension for parents with more than one child. These results confirm that the TFSQ is an adequate and validated instrument for assessing parenting dimensions, as it performs equally well for mothers and fathers, regardless of their parenting experience. There is also support for the notion that parental attitudes to feeding may be related to their socio-economic background.