Latent Model Research Articles

Correction to: Social competencies, classroom relationships, and academic engagement: A latent change score modeling approach among lower secondary school students

Correction to: Social competencies, classroom relationships, and academic engagement: A latent change score modeling approach among lower secondary school students

Longitudinal effects of a professional development program on principals’ leadership practices: results of a baseline latent change score model

ABSTRACT Principal leadership plays an important role in teacher practices and student achievement. Professional development (PD) can help principals to improve their leadership practices – yet few studies to date have examined the effects of formal PD on principals. The present study uses matched data from n = 62 principals and n = 1112 teachers to investigate the longitudinal effects of an 18-month PD program regarding school improvement for principals in Germany. Results from baseline latent change score modelling suggest positive changes in principals’ self-reported leadership practices aimed at improving instructional quality at their schools and promoting teacher development. The positive change in practices is partly reflected in the reports of teachers in the respective schools. We find evidence for characteristics of high-quality PD programs for principals, such as joint participation of leadership pairs, group discussions, internships and coaching, that might help practitioners to design future PD activities for principals. Research should further examine characteristics of effective PD for principals taking control groups and confounding variables into account.

Exploring Research Trends on Digital Health in Nursing Science in Korea: A Topic Modeling Approach.

Digital health technology is utilized in contemporary nursing practice and education. This review explored the scope of digital health applications and major trends in nursing research involving digital health in Korea using topic modeling. Our analysis of data using the Latent Dirichlet Allocation model identified four distinct research topics: nursing education using digital technologies (35.17%), hospital-based nursing practice using digital technologies (19.88%), digital technologies for health education (25.75%), and development of digital technologies to support self-management of chronic conditions (19.20%). Our findings reveal trends, current issues, and gaps in digital health nursing research.

Topic selection for text classification using ensemble topic modeling with grouping, scoring, and modeling approach

TextNetTopics (Yousef et al. in Front Genet 13:893378, 2022. https://doi.org/10.3389/fgene.2022.893378) is a recently developed approach that performs text classification-based topics (a topic is a group of terms or words) extracted from a Latent Dirichlet Allocation topic modeling as features rather than individual words. Following this approach enables TextNetTopics to fulfill dimensionality reduction while preserving and embedding more thematic and semantic information into the text document representations. In this article, we introduced a novel approach, the Ensemble Topic Model for Topic Selection (ENTM-TS), an advancement of TextNetTopics. ENTM-TS integrates multiple topic models using the Grouping, Scoring, and Modeling approach, thereby mitigating the performance variability introduced by employing individual topic modeling methods within TextNetTopics. Additionally, we performed a thorough comparative study to evaluate TextNetTopics’ performance using eleven state-of-the-art topic modeling algorithms. We used the extracted topics for each as input to the G component in the TextNetTopics tool to select the most compelling topic model regarding their predictive behavior for text classification. We conducted our comprehensive evaluation utilizing the Drug-Induced Liver Injury textual dataset from the CAMDA community and the WOS-5736 dataset. The experimental results show that the Latent Semantic Indexing provides comparable performance measures with fewer discriminative features when compared with other topic modeling methods. Moreover, our evaluation reveals that the performance of ENTM-TS surpasses or aligns with the optimal outcomes obtained from individual topic models across the two datasets, establishing it as a robust and effective enhancement in text classification tasks.

Data augmentation in human-centric vision

AbstractThis survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks, a first of its kind in the field. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection, addressing the significant challenges posed by overfitting and limited training data in these domains. Our work categorizes data augmentation methods into two main types: data generation and data perturbation. Data generation covers techniques like graphic engine-based generation, generative model-based generation, and data recombination, while data perturbation is divided into image-level and human-level perturbations. Each method is tailored to the unique requirements of human-centric tasks, with some applicable across multiple areas. Our contributions include an extensive literature review, providing deep insights into the influence of these augmentation techniques in human-centric vision and highlighting the nuances of each method. We also discuss open issues and future directions, such as the integration of advanced generative models like Latent Diffusion Models, for creating more realistic and diverse training data. This survey not only encapsulates the current state of data augmentation in human-centric vision but also charts a course for future research, aiming to develop more robust, accurate, and efficient human-centric vision systems.

Acceptability of linking individual credit, financial, and public records data to healthcare records for suicide risk machine learning models.

Individual-level information about negative life events (NLE) such as bankruptcy, foreclosure, divorce, and criminal arrest might improve the accuracy of machine learning models for suicide risk prediction. Individual-level NLE data is routinely collected by vendors such as Equifax. However, little is known about the acceptability of linking this NLE data to healthcare data. Our objective was to assess preferences for linking external NLE data to healthcare records for suicide prevention. We conducted a discrete choice experiment (DCE) among Kaiser Permanente Washington (KPWA) members. Patient partners assisted in the design and pretesting of the DCE survey. The DCE included 12 choice tasks involving 4 data linking program attributes and 3 levels within each attribute. We estimated latent class conditional logit models to derive preference weights. There were 743 participants. Willingness to link data varied by type of information to be linked, demographic characteristics, and experience with NLE. Overall, 65.1% of people were willing to link data and 34.9% were more private. Trust in KPWA to safeguard data was the strongest predictor of willingness to link data. Most respondents supported linking NLE data for suicide prevention. Contrary to expectations, People of Color and people who reported experience with NLEs were more likely to be willing to link their data. A majority of participants were willing to have their credit and public records data linked to healthcare records provided that conditions are in place to protect privacy and autonomy.

Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models

Latent Gaussian process (GP) models are flexible probabilistic non-parametric function models. Vecchia approximations are accurate approximations for GPs to overcome computational bottlenecks for large data, and the Laplace approximation is a fast method with asymptotic convergence guarantees to approximate marginal likelihoods and posterior predictive distributions for non-Gaussian likelihoods. Unfortunately, the computational complexity of combined Vecchia-Laplace approximations grows faster than linearly in the sample size when used in combination with direct solver methods such as the Cholesky decomposition. Computations with Vecchia-Laplace approximations can thus become prohibitively slow precisely when the approximations are usually the most accurate, i.e., on large data sets. In this article, we present iterative methods to overcome this drawback. Among other things, we introduce and analyze several preconditioners, derive new convergence results, and propose novel methods for accurately approximating predictive variances. We analyze our proposed methods theoretically and in experiments with simulated and real-world data. In particular, we obtain a speed-up of an order of magnitude compared to Cholesky-based calculations and a threefold increase in prediction accuracy in terms of the continuous ranked probability score compared to a state-of-the-art method on a large satellite data set. All methods are implemented in a free C++ software library with high-level Python and R packages. 4

Exploring the relationship between cognition and mental health in intersex participants in the UK Biobank study

Objective: The physical and mental health of intersex individuals is woefully understudied. A recent survey of intersex individuals found high rates of self-reported cognitive issues such as difficulty remembering and concentrating as well as high rates of mental health issues, such as depression and anxiety. Method: The current study explores whether cognitive differences are observed between 353 intersex and over 400,000 non-intersex people using a latent model of cognitive tasks derived from measures in the UK (United Kingdom) Biobank study. Results: There were no differences in intelligence between intersex people and non-intersex people. We found significantly lower executive function and processing speed in intersex individuals versus non-intersex individuals. However, after accounting for mental health differences via regression and case-control matching, there were no significant differences in executive function or processing speed between intersex individuals and non-intersex individuals. Conclusion: Mental health differences between intersex and non-intersex individuals may account for differences in cognitive factor scores.

Exploring the prescription laws of bone flaccidity in ancient chinese medical texts based on association rules and latent structure model

Objective: To analyze the identification and typing of bone flaccidity and the law of medication in ancient books based on the Encyclopaedia of TCM (Traditional Chinese medicine) using the hidden structure model. Methods: The formulations for bone flaccidity were searched in the Chinese Medical Code (software), based on which an Excel sheet was created to conduct frequency analysis and construct a matrix of high-frequency medications. The high-frequency medications for bone flaccidity were then analyzed using the Latent 5.0 software and a latent structure model. With SPSS Modeler and Latent 5.0 as the main analysis methods, Chinese medications with a frequency >1.00% were investigated using the latent structure model and association rules, and the medication rules in treating bone flaccidity with TCM were dissected based on the data of frequency. Results: A total of 223 TCM prescriptions were included, with 270 extracted TCM and a total medication frequency of 2340 times. Among them, Achyranthis Bidentatae Radix, Poria, Angelicae Sinensis Radix, Eucommiae Cortex, Rehmanniae Radix Praeparata, and Ginseng Radix Et Rhizoma. They were the high-frequency medicines for treating bone flaccidity, and the efficacy of tonifying medicinal, heat-clearing medicinal, and blood activating and stasis-resolving medicinal were the high-frequency medicinal categories. Through the analysis of the evidence by formula, it was concluded that spleen-kidney yang deficiency, kidney deficiency and blood stasis, kidney yang deficiency, qi deficiency/qi stagnation and blood stasis, and liver and kidney yin deficiency were the 6 main types of bone flaccidity, which was generally in line with the etiology of bone flaccidity, which was caused by kidney yang deficiency and accompanied by malfunction of the liver and kidneys. In ancient books, the regulation of the kidney is put into an important position. In this study, 19 correlative principles for the treatment of bone flaccidity were derived, whose main combinations are Paeoniae Radix Alba - Angelicae Sinensis Radix, Eucommiae Cortex + Poria-Ginseng Radix Et Rhizoma, Dioscoreae Spongiosae Rhizoma + Cuscutae Semen-Eucommiae Cortex. Conclusion: The medication law of bone flaccidity in the Encyclopaedia of TCM mainly shows that it attaches importance to the regulation of tonifying the liver and kidney to strengthen the tendons and bones and emphasizes that the regulation of the three Zang–fu organs, namely the kidney, the liver and the spleen, can play an important role in the treatment of bone flaccidity.

Sport mental health continuum in young Brazilian athletes: adaptation and psychometric properties

Instruments used to assess the mental well-being of young athletes in Brazil are scarce. Therefore, the present study aimed to translate, cross-culturally adapt for young athletes, and gather evidence of validity for the Sport Mental Health—Short Form (S-MHC) for use in Brazilian Portuguese. The research was conducted in five stages: translation, synthesis, back-translation, expert review, and validation of the psychometric properties. For validation, 246 young athletes of both genders (88 females, 35.8%), aged between 12 and 18 years (14.5 ± 1.9 years), were recruited. Psychometric methods were employed to confirm and validate the translated and adapted versions of the S-MHC for young athletes, including internal consistency using Cronbach's alpha and McDonald's omega, composite reliability, Item Characteristic Curve (ICC) using Item Response Theory (IRT), and Confirmatory Factor Analysis (CFA). Two structures were tested, with Model 1 loading the 14 items of the translated version of the S-MHC into a single latent factor and Model 2 loading the items into three factors related to emotional, social, and psychological sport well-being. Both models showed good validity, consistency, and reliability measures and can be used to investigate the sport well-being of young athletes. It was concluded that the translated version of the S-MHC in Brazilian Portuguese can be used to assess the sport well-being of young athletes in Brazil. Model 2 structure is recommended to observe the different nuances of emotional, social, and psychological well-being.

Uptake of biosimilars in China: a retrospective analysis of the case of trastuzumab from 2018 to 2023

BackgroundThe Chinese biosimilar industry has demonstrated rapid growth in recent years. Limited evidence is available about biosimilar uptake at the national level. This study aimed to assess biosimilar uptake in the case of trastuzumab and to explore potential factors influencing the biosimilar penetration at national and provincial levels.MethodsThis study employed an interrupted time series analysis to assess the level and trend changes of national trastuzumab originator consumption and the overall trastuzumab consumption after the price reduction of the originator and the introduction of the first biosimilar using the China Hospital Pharmacy Audit procurement data from March 2018 to February 2023. A latent class trajectory model (LCTM) was also adopted to estimate the biosimilar penetration across 30 provincial-level administrative divisions (PLADs). Based on the LCTM grouping results, provincial characteristics were analyzed.ResultsAfter rapid growth, the penetration of biosimilars demonstrated a moderate ascending trend at the national level, reaching 27% in February 2023. Following the introduction of the first biosimilar in July 2021, the consumption of the originator decreased by 0.5% per month (P = 0.008), and the growth rate of overall trastuzumab consumption decreased by 1.1% per month (P = 0.014). LCTM fit the best when the number of trajectory classes was two, dividing 30 PLADs into a group demonstrating a faster increase in biosimilar penetration and the other with a slower increase. The PLADs in the fast-increasing group had a higher proportion of the population covered by the national basic health insurance, a lower proportion of the urban population, a lower proportion of the population covered by the urban employee health insurance program, a lower gross domestic product per capita, a lower total health expenditure per capita, and a lower out-of-pocket expenditure.ConclusionsThe uptake of trastuzumab biosimilars in China was lower compared with major European countries. The introduction of trastuzumab biosimilars presented a substitutional effect. Perceptions of physicians and patients, the medicines procurement model, competition from other biologics, and health insurance payment methods may influence biosimilar uptake. Enhancing a comprehensive understanding of biosimilars among physicians and patients, including biologics with biosimilars in the national pooled procurement, and implementing provider payment reforms could foster biosimilar penetration.

Synergistic effects of students’ mathematics and science motivational beliefs on achievement, and their determinants

BackgroundStudents’ mathematics and science motivational beliefs are crucial determinants of their school academic achievement in math and science. The current study aimed to identify the group memberships of students’ motivational beliefs in math and science, which are closely related. Furthermore, this study probed the predictive effects of individual students’ experiences at school on forming group membership. We also tested the mean differences of the identified latent groups in math and science achievement.ResultsUsing latent profile analysis modeling, we examined data from 3857 Korean eighth-grade students participating in the 2019 Trends in International Mathematics and Science Study. The theoretical rationale and supplementary statistical indices showed a five-group membership as the optimal solution. The five groups are high motivation, medium motivation, low math/high science motivation, low motivation, and very low motivation. Students’ sense of school belonging was the most crucial predictor in forming group membership, whereas perceived student bullying did not predict group membership. Finally, students in distinct motivational belief groups exhibited differences in their math and science achievements.ConclusionsThis study identified five subgroups of students based on their distinct motivational beliefs in math and science, and variations in their association with achievements. In terms of policy development and intervention, it is important to nurture students’ sense of school belonging. This study advances motivational theories in science, technology, engineering, and mathematics education, and provides practical suggestions for improving educational practices to enhance student math and science motivational beliefs.

An embedded computational framework of memory: Accounting for the influence of semantic information in verbal short-term memory

We introduce the Embedded Computational Framework of Memory (eCFM), a model that integrates structured semantic word representations with an instance-based memory model to account for the influence of semantic information in verbal short-term memory. The eCFM combines principles from the episodic MINERVA 2 model and the semantic Latent Semantic Analysis model. After reviewing how semantic information impacts verbal short-term memory performance, we demonstrate eCFM’s ability to reconcile various phenomena within a common computational framework. Our model captures key findings, such as the influence of semantic information in serial recall, its reduction in serial reconstruction, and the impact of task difficulty on semantic information. In five experiments, we tested predictions derived from the eCFM. Experiments 1 and 2 manipulated list organization, with Experiment 1 using alternating lists of related or unrelated words and Experiment 2 using blocked lists. Experiment 3 varied presentation rates, Experiment 4 revisited the detrimental effect of semantic information on order information, and Experiment 5 explored false recall. We found that semantic information interacts with list composition, presentation rate affects the magnitude of its influence, and semantic information impacts order information contrary to the dominant view. Additionally, increasing the number of related study words to a non-studied semantic lure boosts false recall. The eCFM captured these findings as well as memory at the item level. Our demonstration provides insight into the cognitive mechanisms underlying verbal short-term memory and the interplay of semantic and episodic memory processes in recall.

The impact of trauma core dimensions on anxiety and depression: a latent regression model through the Post-Traumatic Symptom Questionnaire (PTSQ)

Adverse life events (e.g., severe accidents, violence/abuse, organic disorders) can elicit traumatic responses characterized by intrusive thoughts, hyperarousal, and avoidance—highlighting the need for sound assessment tools. Also, these traumatic components could heighten anxiety and depression symptoms. This study aims included to: (1) assessing the psychometric properties of the Post-Traumatic Symptom Questionnaire (PTSQ) and delineating clinical cut-offs; (2) investigating how distinct trauma components contribute to anxiety and depression symptoms. Involving 761 participants who experienced a traumatic event, Part I tested the PTSQ psychometric properties, defining clinical cut-offs. Part II tested the impact of traumatic components on anxiety and depression symptoms, using a multiple multivariate latent regression model. PTSQ exhibited exemplary fit indices and robust psychometric properties. Clinically relevant cut-offs were identified. The differential contributions of intrusion, avoidance, and hyperarousal to anxiety and depression symptoms were evaluated, elucidating the strength and nature of these relationships. This study reaffirms the PTSQ as a psychometrically sound and reliable instrument. It underscores the effects of intrusion, avoidance, and hyperarousal on anxiety and depression symptoms in individuals with traumatic experiences. These insights advocate for evidence-based interventions aimed at alleviating the psychological suffering associated with trauma components, fostering adaptation and supporting psychological health.

Multidimensional patterns of early care and education access through a family centered lens

Despite evidence of the benefits of early care and education (ECE) for child development and family employment, the supply of providers is scarce and variable; leading many families with young children to experience limited and inequitable access. To examine the multidetermined nature of access, this study leverages a multidimensional, family-centered definition and a nationally representative sample of families of preschoolers from the Early Childhood Longitudinal Study – Birth Cohort to examine (1) patterns of access-related features present in preschool-age children's ECE arrangements, and (2) child characteristics, household circumstances, and care setting type that may be related to patterns of access. Latent class analysis model enumeration revealed five latent classes of children's nonparental care arrangements along features of accessibility: High Across Most Access Features (26.4 %), Single, Long-Term Provider (30.1 %), Limited Across Most Access Features (11.6 %), Low Affordability, Multiple Providers (10.1 %), and High Affordability, Recent Transition (21.8 %). These results suggested many children were in ECE that met multiple dimensions of access, with others in care arrangements that reflected trade-offs. Children were also differentially classified into types of ECE arrangements in relation to care setting type, race/ethnicity, income, household urbanicity, and parental employment. The implications of investigating ECE access as a multidimensional construct, and recommendations for how ECE providers and policy can more closely align with family needs are discussed.

Life's Essential 8 Trajectories and Risk of Stroke: A Prospective Cohort Study.

Evidence is lacking regarding long-term patterns of change in Life's Essential 8 (LE8) and their association with the risk of stroke. We aim to evaluate LE8 trajectories and examine their association with the risk of stroke in China. This study, conducted in a workplace setting, recruited 26 719 participants (average age, 46.02±11.27 years and a male population of 73.73%) who had no history of stroke and consecutively participated in 6 surveys from 2006 to 2016. Repeated LE8 measurements were determined by taking the unweighted average of the 8 component scores ranging from 0 to 100. People with higher scores had better overall cardiovascular health. By examining the medical records of the participants, stroke cases were identified for the period from 2016 to 2020. A latent mixture model was applied to classify the trajectory clusters of LE8 from 2006 to 2016, and Cox proportional hazard models were used to analyze the data. Five LE8 trajectories were detected between 2006 and 2016. Four hundred ninety-eight incident strokes including 55 (11.04%) hemorrhagic and 458 (91.97%) ischemic strokes were documented. After adjusting for covariates, the hazard ratios and 95% CIs for the association between stable-low, moderate-increasing, moderate-stable, and high-stable trajectories and incident stroke, compared with the moderate-decreasing trajectory, were 1.42 (1.11-1.84), 0.73 (0.56-0.96), 0.49 (0.39-0.62), and 0.19 (0.11-0.32), respectively. Individuals with high LE8 status (LE8≥80) exhibited a significantly reduced risk of stroke compared with those with low one (LE8≤49; P-trend <0.001). A faster annual growth in LE8 was related to a lower risk of stroke. Maintaining high LE8 over an extended period and high baseline LE8 status were related to a decreased risk of stroke. Despite the initial low level of LE8, improvement in LE8 attenuates or even reverses the risk of stroke.

Predicting CDR status over 36 months with a recall-based digital cognitive biomarker.

Word-list recall tests are routinely used for cognitive assessment, and process scoring may improve their accuracy. We examined whether Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) derived, process-based digital cognitive biomarkers (DCBs) at baseline predicted Clinical Dementia Rating (CDR) longitudinally and compared them to standard metrics. Analyses were performed with Alzheimer's Disease Neuroimaging Initiative (ADNI) data from 330 participants (mean age=71.4±7.2). We conducted regression analyses predicting CDR at 36 months, controlling for demographics and genetic risk, with ADAS-Cog traditional scores and DCBs as predictors. The best predictor of CDR at 36 months was M, a DCB reflecting recall ability (area under the curve =0.84), outperforming traditional scores. Diagnostic results suggest that M may be particularly useful to identify individuals who are unlikely to decline. These results suggest that M outperforms ADAS-Cog traditional metrics and supports process scoring for word-list recall tests. More research is needed to determine further applicability with other tests and populations. Process scoring and latent modeling were more effective than traditional scoring. Latent recall ability (M) was the best predictor of Clinical Dementia Rating decline at 36 months. The top digital cognitive biomarker model had odds ≈ 90 times greater than the top Alzheimer's Disease Assessment Scale-Cognitive subscale model. Particularly high negative predictive value supports literature on cognitive testing as a useful screen. Consideration of both cognitive and pathological outcomes is needed.

Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning

Identifying the time-varying control schemes that maximize storage performance is critical to the commercial deployment of geological carbon storage (GCS) projects. However, the optimization process typically demands extensive resource-intensive simulation evaluations, which poses significant computational challenges and practical limitations. In this study, we presented the multimodal latent dynamic (MLD) model, a novel deep learning framework for fast flow prediction and well control optimization in GCS operations. The MLD model implicitly characterizes the forward compositional simulation process through three components: a representation module that learns compressed latent representations of the system, a transition module that approximates the evolution of the system states in the low-dimensional latent space, and a prediction module that forecasts the flow responses for given well controls. A novel model training strategy combining a regression loss and a joint-embedding consistency loss was introduced to jointly optimize the three modules, which enhances the temporal consistency of the learned representations and ensures multi-step prediction accuracy. Unlike most existing deep learning models designed for systems with specific parameters, the MLD model supports arbitrary input modalities, thereby enabling comprehensive consideration of interactions between diverse types of data, including dynamic state variables, static spatial system parameters, rock and fluid properties, as well as external well settings. Since the MLD model mirrors the structure of a Markov decision process (MDP) that computes state transitions and rewards (i.e., economic calculation for flow responses) for given states and actions, it can serve as an interactive environment to train deep reinforcement learning agents. Specifically, the soft actor-critic (SAC) algorithm was employed to learn an optimal control policy that maximizes the net present value (NPV) from the experiences gained by continuous interactions with the MLD model. The efficacy of the proposed approach was first compared against commonly used simulation-based evolutionary algorithm and surrogate-assisted evolutionary algorithm on a deterministic GCS optimization case, showing that the proposed approach achieves the highest NPV, while reducing the required computational resources by more than 60%. The framework was further applied to the generalizable GCS optimization case. The results indicate that the trained agent is capable of harnessing the knowledge learned from previous scenarios to provide improved decisions for newly encountered scenarios, demonstrating promising generalization performance.

Tests of moderation of the big-fish-little-pond effect across students with and without special educational needs in regular versus special education schools

BackgroundInclusive education has become increasingly popular based on the assumption that it has multiple benefits for students with special educational needs (SEN). However, contradictions remain regarding the widespread nature of these benefits, particularly when it comes to academic motivation. AimsIn this large-scale cross-sectional study, we relied on the Big-Fish-Little-Pond Effect (BFLPE) to assess the links between inclusive education and students’ academic self-concepts and learning interests in the mathematics and verbal domains. SampleThe sample consisted of 21,219 German elementary school children in Grade 4 who were enrolled in three groups: students without SEN attending regular schools (n1 = 19,069), students with SEN attending regular schools (n2 = 933), and students with SEN attending special education schools (n3 = 1214). MethodsDoubly latent multi-group multilevel structural equation models and tests of latent interaction were performed to test the BFLPE. ResultsResults supported the BFLPE for both outcomes and domains. However, no BFLPE was identified for learning interest in the verbal domain among students with SEN attending special education schools, although the size of this effect did not differ significantly from that observed among students without SEN. In regular schools, the BFLPE was almost two times stronger among students with SEN than among their peers without SEN. ConclusionsResults support the generalizability of the BFLPE to students with SEN, while casting doubts on the motivational benefits of inclusive education for these students. Interventions targeted at attenuating the BFLPE should thus be tailored for both regular and special education schools.

Coping Strategies of Patients With Advanced Lung or Colorectal Cancer Over Time: Insights From the International ACTION Study.

A comprehensive understanding of coping strategies of patients with advanced diseases can contribute to providing supportive care that meets patients' needs. However, insight into how coping of this population develops over time is lacking. We examined coping strategies of patients with advanced cancer over time and identified distinct trajectories and their predictors. Data from 675 patients of the control group from the ACTION cluster-randomized trial were analyzed. Patients with lung or colorectal cancer from six European countries (Belgium, Denmark, Italy, the Netherlands, Slovenia and the United Kingdom) completed questionnaires at baseline, 12 and 20weeks. Measures included Denial, Acceptance, and Problem-focused coping (COPE, Brief COPE inventory; scores 4-16 per scale). We used linear mixed models to analyze the data and latent class mixed models to identify stable (within patient change <2) coping strategies. At baseline, patients reported low use of Denial (6.6) and greater use of Acceptance (12.6) and Problem-Focused coping (12.2). These scores did not significantly change. We found four distinct trajectories for the use of Denial, three for Acceptance and five for Problem-Focused coping strategies. Stable trajectories were found in 513 (77%) patients for Denial, 645 (96%) for Acceptance and 602 (91%) for Problem-Focused coping. All coping strategies were stable in 447 (68%) patients and two were stable in 181 patients (28%). Overall, the use of coping strategies was rather stable in the majority of patients with advanced cancer. However, for each of the coping strategies subgroups of patients reported fluctuating coping trajectories.