bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptation of the Disaster Literacy Scale to Individuals Aged 60 Years and Older and Exploring its Psychometric Properties
Authors: Güzel Aysun 
Published: January 2025

Abstract

Objectives
This methodological study aimed to adapt the DLS, introduced for individuals aged 18-60 years, to those aged 60 years and older and to determine its psychometric properties.

Methods
We collected the data between December 15, 2021 and April 18, 2022. We carried out the study with a sample of 60 years and older living in the city center of Burdur, Turkey. The sample was selected using snowball sampling, a non-probability sampling technique. We collected the data using a questionnaire booklet covering an 11-item demographic information form and the DLS. We utilized reliability and validity analyses in the data analysis. The analyses were performed on SPSS 23.0, and a P value < 0.05 was considered statistically significant.

Results
The mean age of the participants was found to be 68.29 (SD = 6.36). The 61-item measurement tool was reduced to 57 items by removing a total of 4 items from the scale. We also calculated Cronbach’s α values to be 0.936 for the mitigation/prevention subscale, 0.935 for the preparedness subscale, 0.939 for the response subscale, and 0.945 for the recovery/rehabilitation subscale.

Conclusions
As adapted in this study, the DLS-S can be validly and reliably used for individuals aged 60 years and older.
The Assessment of Post-Earthquake Quality of Life of Earthquake Survivors
Authors: Dincer Metin , Akyol Mesut , Karatas Yunus Emre , Kocakaya Mustafa 
Published: January 2025

Abstract

Objectives
This study aims to assess the quality of life (QoL) of earthquake survivors who experienced 2 major earthquakes in Türkiye on February 6, 2023, with different measurement tools.

Methods
The study was conducted in 2 centers with a total of 467 participants. For QoL measurement, face-to-face Euroqol EQ-5D-5L, Euroqol Visual Analog Scale (EQ-VAS), and Nottingham Health Profile (NHP) measurement tools were used.

Results
On the EQ-5D-5L scale, most of the participants stated that they struggled the most with the anxiety/Depression dimension. In NHP, the highest mean score is in the Emotional Reaction section. Females and the 55+ age group were found to have worse QoL. A linear and moderate correlation was found between the EQ-5D-5L index value and the EQ-VAS score; an inverse, weak, or moderate correlation was found between the EQ-VAS score and the sections of the NHP scores; an inverse and strong correlation was found between the Nottingham Health Profile Distress (NHP-D) score and the EQ-5D-5L index value.

Conclusions
The findings obtained with the measurement tools used in this study reveal various dimensions affecting the QoL of different cohorts. In addition, the study provides important evidence for policies to be developed to increase post-earthquake QoL.
The Generalized Cognitive Diagnosis Model Framework for Polytomous Attributes
Authors: de la Torre Jimmy , Qiu Xuelan , Santos Kevin Carl 
Published: January 2025

Abstract
For classroom teaching and learning, classifying students’ skills into more than two categories (e.g., no, basic, and advanced masteries) is more instructionally relevant. Such classifications require polytomous attributes, for which most existing cognitive diagnosis models (CDMs) are inapplicable. This paper proposes the saturated polytomous cognitive diagnosis model (sp-CDM), a general model that subsumes existing CDMs for polytomous attributes as special cases. The generalization is shown by mathematically illustrating the relationships between the proposed and existing CDMs. Moreover, algorithms to estimate the proposed model is proposed. A simulation study is conducted to evaluate the parameter recovery of the sp-CDM using the proposed estimation algorithms, as well as to illustrate the consequences of improperly fitting constrained or unnecessarily complex polytomous-attribute CDMs. A real-data example involving polytomous attributes is presented to demonstrate the practical utility of the proposed model.
A Family of Sequential Item Response Models for Multiple-Choice, Multiple-Attempt Test Items
Authors: Lu Yikai , Fowler Jim , Cheng Ying 
Published: January 2025

Abstract
We consider a test which allows students to attempt a multiple-choice question multiple times (i.e., multiple attempts). The most extreme form of multiple attempts is the answer-until-correct (AUC) procedure. Previous research has demonstrated that multiple-attempt procedures such as AUC could potentially enhance learning and increase reliability. However, for multiple-choice items, guessing is still non-ignorable. Traditional models of sequential item response theory (SIRT) could model multiple-attempt procedures but fail to take guessing into account. The purpose of this study is to propose SIRT models for multiple-choice, multiple-attempt items (SIRT-MM). First, we defined a family of SIRT-MM models to account for the idiosyncrasies of items, answer options, and examinee behavior. We also explained how these models could improve person parameter estimates by taking into account partial (mis)information of examinees. Second, we conducted model comparisons between the SIRT-MM models, the graded response model, and the nominal response model. Third, we discussed how the item and person parameters can be estimated, and evaluated item and person parameter recovery of SIRT-MM models under different conditions through a simulation study. Finally, we applied the SIRT-MM models to a real dataset and demonstrated their utility through model selection, person parameter recovery, and information functions.
Deriving Models of Change with Interpretable Parameters: Linear Estimation with Nonlinear Inference
Authors: McCormick Ethan M. 
Published: January 2025

Abstract
In modeling change over time, developmental theories often emphasize meaningful quantities like peaks, inflections, timing, and tempo. However, longitudinal analyses typically rely on simple polynomial models that estimate powered terms of time in a linear, additive form which are disconnected from these meaningful quantities. While these linear parameterizations are computationally efficient and produce stable results, the quantities estimated in these models are difficult to directly connect to theoretical hypotheses. To address this disconnect between estimation and theory development, I propose several approaches for linear estimation with nonlinear inference (LENI), a framework that transforms results from stable, easily-estimated linear models into nonlinear estimates which align with theoretical quantities of interest through a set of principled transformation functions. I first outline derivations for the interpretable nonlinear parameters, and transform the results of the corresponding linear model—including fixed and random effects as well as conditional covariates effects —into the results we would have obtained by fitting a nonlinear version of the model. I conclude by summarizing a linearized structural equation model approach which can flexibly accommodate any known nonlinear target function within a linearly-estimable framework. I conclude with recommendations for applied researchers and directions for fruitful future work in this area.
Generalized Bayesian method for diagnostic classification models
Authors: Yamaguchi Kazuhiro , Liu Yanlong , Xu Gongjun 
Published: January 2025

Abstract
This study extends the loss function-based parameter estimation method for diagnostic classification models proposed by Ma, de la Torre, et al. (2023, Psychometrika) to consider prior knowledge and uncertainty of sampling. To this end, we integrate the loss function-based estimation method with the generalized Bayesian method. We establish the consistency of attribute mastery patterns of the proposed generalized Bayesian method. The proposed generalized Bayesian method is compared in a simulation study and found to be superior to the previous nonparametric diagnostic classification method—a special case of the loss function-based method. Moreover, the proposed method is applied to real data and compared with previous parametric and nonparametric estimation methods. Finally, practical guidelines for the proposed method and future research directions are discussed.
Implications of Alternative Parameterizations in Structural Equation Models for Longitudinal Categorical Variables
Authors: Bianconcini Silvia , Bollen Kenneth A. 
Published: January 2025

Abstract
When analyzing scaling conditions in latent variable structural equation models (SEMs) with continuous observed variables, analysts scaling a latent variable typically set the factor loading of one indicator to one and either set its intercept to zero or the mean of its latent variable to zero. When binary and ordinal observed variables are part of SEMs, the identification and scaling choices are more varied and multifaceted. Longitudinal data further complicate this. In SEM software, such as lavaan and Mplus, fixing the underlying variables’ variances or the error variances to one are two primary scaling conventions. As demonstrated in this paper, choosing between these constraints can significantly impact longitudinal analysis, affecting model fit, degrees of freedom, and assumptions about the dynamic process and error structure. We explore alternative parameterizations and conditions of model equivalence with categorical repeated measures.
Using data from the National Longitudinal Survey of Youth 1997, we empirically explore how different parameterizations lead to varying conclusions in longitudinal categorical analysis. More specifically, we provide insights into the specifications of the autoregressive latent trajectory model and its special cases—the linear growth curve and first-order autoregressive models—for categorical repeated measures. These findings have broader implications for a wide range of longitudinal models.
Goodbye human annotators? Content analysis of social policy debates using ChatGPT
Authors: Gielens Erwin , Sowula Jakub , Leifeld Philip 
Published: January 2025

Abstract
Content analysis is a valuable tool for analysing policy discourse, but annotation by humans is costly and time consuming. ChatGPT is a potentially valuable tool to partially automate content analysis for policy debates, largely replacing human annotators. We evaluate ChatGPT’s ability to classify documents using pre-defined argument descriptions, comparing its performance with human annotators for two policy debates: the Universal Basic Income debate on Dutch Twitter (2014–2016) and the pension reforms debate in German newspapers (1993–2001). We use the API (GPT-4 Turbo) and user interface version (GPT-4) and evaluate multiple performance metrics (accuracy, precision and recall). ChatGPT is highly reliable and accurate in classifying pre-defined arguments across datasets. However, precision and recall are much lower, and vary strongly between arguments. These results hold for both datasets, despite differences in language and media type. Moreover, the cut-off method proposed in this paper may aid researchers in navigating the trade-off between detection and noise. Overall, we do not (yet) recommend a blind application of ChatGPT to classify arguments in policy debates. Those interested in adopting this tool should manually validate bot classifications before using them in further analyses. At least for now, human annotators are here to stay.
Rights to Her Labor: Women Workers on Mexico’s Southeastern Railroads
Authors: Reed Kate 
Published: January 2025

Abstract
Across the twentieth century, hundreds of women worked as nurses, cooks, cleaners, and teachers on Mexico’s railroads. They have been overlooked in histories of the railroads and Mexican industrialization more broadly, their limited number perhaps suggesting that their work is not of analytical importance in understanding processes of economic development and class formation. On the contrary, these women’s work constituted many of the most coveted labor rights of the postrevolutionary railroad workforce, itself a symbolic vanguard of Mexico’s working class and one of the most important beneficiaries of the expansion of social and economic rights ushered in by the Mexican Revolution. The gendered division of labor characteristic of the railroads was neither accidental nor insignificant. Railroads used the feminization of the work of social reproduction to write off structural failures and predictable shortcomings in welfare provision as failures of femininity. Women became scapegoats for the consistent violation of workers’ rights through underfunding and understaffing. In tracing this process, the article models a historiographical and methodological intervention with broader relevance. It suggests that the social and labor rights that expanded around the world in the late-nineteenth and early-twentieth centuries must be studied not only from the vantage of legal or political history, but as themselves questions of social and labor history. Making these rights real depended on socially reproductive work that has often been marginal in accounts of industrialization and economic development. It is impossible to understand the political economy of social and economic rights without understanding women’s work.
Diagnostic accuracy of the Montreal Cognitive Assessment in screening for cognitive impairment in initially hospitalized COVID-19 patients: Findings from the prospective multicenter NeNeSCo study
Authors: Klinkhammer Simona , Verwijk Esmée , Geurtsen Gert , Duits Annelien A. , Matopoulos Georgios , Visser-Meily Johanna M.A. , Horn Janneke , Slooter Arjen J.C. , van Heugten Caroline M. ,   NeNeSCo study group
Published: January 2025

Abstract

Objective:
This study aimed to investigate the prevalence and nature of cognitive impairment among severely ill COVID-19 patients and the effectiveness of the Montreal Cognitive Assessment (MoCA) in detecting it.

Method:
We evaluated cognition in COVID-19 patients hospitalized during the first wave (March to June 2020) from six Dutch hospitals, nine months post-discharge, using a comprehensive multi-domain neuropsychological test battery. Test performance was corrected for sex, age, and education differences and transformed into z-scores. Scores within each cognitive domain were averaged and categorized as average and above (z-score ≥ −0.84), low average (z-score −1.28 to −0.84), below average (z-score −1.65 to −1.28), and exceptionally low (z-score < −1.65). Patients were classified with cognitive impairment if at least one domain’s z-score fell below −1.65. We assessed the MoCA’s accuracy using both the original cutoff (<26) and an “optimal” cutoff determined by Youden’s index.

Results:
Cognitive impairment was found in 12.1% (24/199) of patients, with verbal memory and mental speed most affected (6.5% and 7% below −1.65, respectively). The MoCA had an area under the curve of 0.84. The original cutoff showed sensitivity of 83% and specificity of 66%. Using the identified optimal cutoff of <24, maintained sensitivity while improving specificity to 81%.

Conclusions:
Cognitive impairment prevalence in initially hospitalized COVID-19 patients is lower than initially expected. Verbal memory and processing speed are primarily affected. The MoCA is a valuable screening tool for these impairments and lowering the MoCA cutoff to <24 improves specificity.
Rainfall‐Runoff Modeling in Rocky Headwater Catchments for the Prediction of Debris Flow Occurrence
Authors: Bernard Martino , Barbini Matteo , Berti Matteo , Boreggio Mauro , Simoni Alessandro , Gregoretti Carlo 
Published: January 2025

AbstractIn the Dolomites, steep rocky cliffs are marked by numerous narrow gullies. When high‐intensity short‐duration precipitation occurs, these gullies concentrate and direct surface runoff to the screes at the foot of rock cliffs. Surface runoff mixes with loose sediments, creating a solid‐liquid surge that, as it moves downhill, increases its volume entraining debris material and transforms into a granular debris flow. Given the ongoing challenge of modeling the relationship between intense rainfall, surface runoff, and debris flow initiation, we take advantage of data from three monitoring stations operating in distinct debris flow active catchments in our study area to make progress. These stations, strategically positioned close to debris flows initiation zones, record videos and different types of flow‐stage data, helping us pinpoint the timing and form of incoming discharge hydrographs. Over a 15‐year period of observation, we collected a comprehensive data set on runoff and mass movement in these catchments, offering valuable insights into their hydrological behavior and the initiation of granular debris flows. To compute infiltration excess runoff generation, we refined an already existing hydrological model and calibrated it using discharge measured at one of the monitoring stations. Testing this updated model against observations from two other larger debris flow sites showed that it can reproduce the initial phases of a debris flow, when sediment concentration rapidly rises. These findings suggest that a well‐tuned hydrological model can predict the discharge from intense, short rainfall events that typically trigger debris flows, as well as the early stages of these phenomena.
Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods
Authors: Harvey Natasha , Burns Sean P. , Musselman Keith N. , Barnard Holly , Blanken Peter D. 
Published: January 2025

AbstractThe interception of snow by the canopy is an important process in the water and energy balance in cold‐region coniferous forests. Direct measurements of canopy snow interception are difficult at scales larger than individual trees, requiring indirect methods such as eddy covariance, time‐lapse photography, or modeling. At the Niwot Ridge Subalpine Forest AmeriFlux site in the Colorado Front Range, USA, we compared methods that estimate or simulate the presence of snow interception. Timelapse photography images were analyzed using thresholding analysis and used to train a Convolutional Neural Network (CNN) model to estimate canopy snow presence. Interception was also estimated from eddy covariance measurements above and below the canopy, as well as from model simulations. These methods were applied over January 2019, with binarized results compared to a “ground truth” of human labeled images to calculate the Balanced Accuracy Score. The highest accuracy was achieved by the CNN predictions. Based on the Balanced Accuracy Scores, select methods were extended to estimate the presence of canopy snow for the 2018/2019 winter. All methods provided insight into the process of interception in a subalpine forest but presented challenges, including differing flux footprints of the above‐ and below‐canopy eddy covariance measurements and the inability of red‐green‐blue imagery to monitor snow interception at night, during sunrise, and during sunset.
Solar Semidiurnal Variations in the Thermosphere and Ionosphere Forced From Above and From Below
Authors: Forbes Jeffrey M. , Zhang Xiaoli , Cullens Chihoko , Maute Astrid 
Published: January 2025

AbstractThe nature and origins of solar‐synchronous semidiurnal variations (SW2) in the thermosphere and ionosphere are explored through analysis of Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) simulations that include and exclude forcing by tides at its 97‐km lower boundary derived from Ionospheric Connection (ICON) explorer data. SW2 consists of two components, one excited in‐situ () and one due to vertical propagation from below the thermosphere (). in turn consists of a part forced by heating due to absorption of solar radiation () and a part of “geomagnetic” origin () arising from magnetospheric coupling at high latitudes. At 300 km all three components achieve wind and temperature amplitudes of order 25 and 15–20 K within specific geographic domains and levels of solar activity. exhibits upward propagation from the lower thermosphere much like the symmetric component of , refraction toward the winter hemisphere by mean winds, modulation by the 27‐day solar rotation rate, and is estimated to exceed in general importance for F10.7 133 sfu. At solar flux levels 75 sfu F10.7 125 sfu, electron density perturbations are of order 10%–35% for and 15%–25% for . Vertical E B drifts are of order 5–8 for , 3–5 for , and 5–8 for , the latter extending to the equator at elevated magnetic activity, and suggesting the presence of penetrating electric fields and/or disturbance dynamo action.
Improved Correction of Extreme Precipitation Through Explicit and Continuous Nonstationarity Treatment and the Metastatistical Approach
Authors: Vidrio‐Sahagún Cuauhtémoc Tonatiuh , He Jianxun , Pietroniro Alain 
Published: January 2025

AbstractClimate models simulate extreme precipitation under nonstationarity due to continuous climate change. However, systematic errors in local‐scale climate projections are often corrected using stationary or quasi‐stationary methods without explicit and continuous nonstationarity treatment, like quantile mapping (QM), detrended QM, and quantile delta mapping. To bridge this gap, we introduce nonstationary QM (NS‐QM) and its simplified version for consistent nonstationarity patterns (CNS‐QM). Besides, correction approaches for extremes often rely on limited extreme‐event records. To leverage ordinary‐event information while focusing on extremes, we propose integrating the simplified Metastatistical extreme value (SMEV) distribution into NS‐QM and CNS‐QM (NS‐QM‐SMEV and CNS‐QM‐SMEV). We demonstrate the superiority of NS‐ and CNS‐QM‐SMEV over existing methods through a simulation study and show several real‐world applications using high‐resolution‐regional and coarse‐resolution‐global climate models. NS‐QM and CNS‐QM reflect nonstationarity more realistically but may encounter challenges due to data limitations like estimation errors and uncertainty, particularly for the most extreme events. These issues, shared by existing approaches, are effectively mitigated using the SMEV distribution. NS‐ and CNS‐QM‐SMEV offer lower estimation error, approximate unbiasedness, reduced uncertainty, and improved representation of the entire distribution, especially for samples of ∼70 years, and greater superiority with larger samples. We show existing methods may perform competitively for short samples but exhibit substantial biases in quantile‐quantile matching due to bypassing nonstationarity modeling. NS‐ and CNS‐QM‐SMEV avoid these biases, adhering better to their theoretical functioning. Thus, NS‐ and CNS‐QM‐SMEV enhance the correction of extremes under nonstationarity. Yet, properly identifying nonstationarity patterns is crucial for reliable implementations.