Search Results (73)

Purpose: Research drives clinical advancement in oral and craniomaxillofacial surgery by generating evidence that guides practice and innovation. However, limited literature exists describing research engagement among surgeons within AO CMF in the Middle East and North Africa. This study evaluated awareness, participation, and perceived barriers to research among AO CMF members and affiliated surgeons in the MENA region. Methods: A cross-sectional, questionnaire-based survey was distributed electronically to AO CMF members, affiliates, and professional CMF surgeon networks between October and December 2024. The 14-item survey assessed demographics, research awareness, attitudes, productivity, and barriers. Responses were anonymized and analyzed descriptively using SurveyPlanet analytics. Results: A total of 144 surgeons from 21 countries completed the survey. Pakistan (35%), Morocco (9.8%), Kuwait (7.7%), and the United Arab Emirates (7%) contributed the largest proportions. Most respondents (47.6%) expressed strong interest in research but reported difficulty initiating projects, while 32.2% cited lack of time as a major constraint. The most frequently reported barriers included challenges in research methodology (14.6%), publishing (14.6%), and manuscript writing (14.1%). Only 18.9% of participants had published more than ten articles, while 29.4% had none. Mentorship demand was high (94.4%), but awareness of the AO PEER program remained limited (37.8%). Conclusion: Surgeons expressed strong enthusiasm for research yet face substantial barriers. Strengthening research methodology training, establishing structured mentorship, expanding AO PEER engagement, and facilitating multicenter collaboration are key strategies to enhance research productivity across the region. Full article

Constructed-response items offer rich evidence of writing proficiency, but the linguistic signals they contain vary with grade level. This study presents a cross-sectional analysis of 5638 English Language Arts essays from Grades 6–12 to identify which linguistic features predict proficiency and to characterize how their importance shifts across grade levels. We extracted a suite of lexical, syntactic, and semantic-cohesion features, and evaluated their predictive power using an interpretive dual-model framework combining LASSO and XGBoost algorithms. Feature importance was assessed through LASSO coefficients, XGBoost Gain scores, and SHAP values, and interpreted by isolating both consensus and divergences of the three metrics. Results show moderate, generalizable predictive signals in Grades 6–8, but no generalizable predictive power was found in the Grades 9–12 cohort. Across the middle grades, three findings achieved strong consensus. Essay length, syntactic density, and global semantic organization served as strong predictors of writing proficiency. Lexical diversity emerged as a key divergent feature, it was a top predictor for XGBoost but ignored by LASSO, suggesting its contribution depends on interactions with other features. These findings inform actionable, grade-sensitive feedback, highlighting stable, diagnostic targets for middle school while cautioning that discourse-level features are necessary to model high-school writing. Full article

This study examines how higher education students use generative artificial intelligence (GenAI) for academic tasks and identifies the types of tasks in which these tools are employed. Our research was conducted in a large multidisciplinary university in Israel. Data from 825 eligible responses to an open-ended item underwent a conventional content analysis using a bottom-up coding approach; data were coded inductively in an iterative process, achieving substantial inter-rater reliability. The findings produced the SOI-MARSMeLLAW framework, which maps eight themes of GenAI use (by frequency, from higher to lowest)—Writing, Learning, Reading, Searching, Meta-learning, Multimedia, Analysis, and Learning Aids—across three information channels: Self, Output, and Input. Overall, we find that students rely on GenAI primarily to support rather than replace their learning, suggesting a flexible and strategic approach that balances efficiency with agency. Two follow-up analyses using the broad data looked at domain-specificity and Task-Centered GenAI Literacy. We found that STEM students emphasizing Learning tasks and non-STEM students highlighting Reading, Meta-learning, and Searching; and that literacy was higher for Writing-related tasks than for Learning-related tasks. These findings have implications for GenAI policy in higher education institutions, and for the redesign of pedagogy and assessment in higher education. Full article

This study examined the psychometric properties of the Pre-Literacy Test, developed to measure the literacy readiness skills of children who have completed preschool education. Using a quantitative, multistage design, the study was conducted with a total of 5966 children aged 6–7 who were about to enter elementary school in the 2024–2025 academic year (N₁ = 1911; N₂ = 1644; N₃ = 2411). Exploratory Factor Analysis revealed a three-factor structure—Reading Skills, Writing Skills (Dictation), and Writing Skills (Copying)—which explained 82.38% of the total variance. Confirmatory Factor Analysis demonstrated that this structure showed an acceptable model fit (CFI = 0.997, TLI = 0.997, SRMR = 0.030, RMSEA = 0.111). The internal consistency coefficients (α = 0.891–0.962; ω = 0.912–0.983) and convergent validity values (AVE = 0.867–0.949) of the PLT were found to be high. Discriminant validity was confirmed according to the Fornell–Larcker criterion, and measurement invariance across gender was supported through Multigroup Confirmatory Factor Analysis. Item analyses indicated that most test items were of moderate difficulty (mean difficulty = 0.409) and high discrimination (mean discrimination = 0.516). In conclusion, the PLT was determined to be a psychometrically robust, valid, and reliable instrument for assessing basic literacy skills prior to elementary school entry. These findings suggest that the test can be confidently used in early literacy research and school readiness assessments. Full article

Review-aware recommendation systems aim to enhance recommendation performance by leveraging user reviews and their associated attributes to model user preferences. However, most existing methods fail to address two critical challenges introduced by user reviews: polarity bias and temporal dynamics. Polarity bias refers to inconsistencies between a user’s numerical ratings and the sentiment expressed in their reviews—for example, a user might give a restaurant a high rating while writing a negative review. In addition, user preferences may evolve over time, as individuals can review the same item on multiple occasions. To address these issues, we propose RARPT, a review-aware recommendation framework that jointly models polarity and temporality. Specifically, we process positive and negative reviews separately and employ a sequential model to capture the temporal evolution of user preferences. We also introduce a polarity balance module, which uses a cross-attention mechanism to generate supplementary collaborative vectors from reviews of the opposite polarity, thereby mitigating both quantitative and relational imbalances. We conduct extensive experiments on two real-world datasets from Amazon and Yelp. The results show that our proposed model significantly outperforms several state-of-the-art baselines. Moreover, our model offers enhanced interpretability, helping deliver more effective personalized recommendations. Full article

Inequalities in enrollment in STEM persist for those entering higher education as first-generation college students, underserved racial and ethnic groups, female and nonbinary individuals, and those from lower socioeconomic backgrounds. The current study aims to better understand the relationship students have with graduate school success factors by redistributing the Graduate Student Success Survey+ (GSSS+) at an R2 institution in the southeastern United States. Exploratory factor analysis was used to test the survey’s validity, with 242 participants. A 7-factor, 40-item model was developed, comprising the following subscales: mentor support, peer support, imposter phenomenon, financial support, microaggressions (related to race and gender), access and opportunity (for research, writing, and presentations), and resilience. Item analysis identified perceived barriers (e.g., microaggressions, imposter phenomenon, and financial stress) for underserved students (i.e., females, underserved racial and ethnic groups, and part-time students). Regression analysis on resilience revealed a positive relationship with mentor support, peer support, and financial support. A negative relationship with resilience was associated with a greater perception of imposter phenomenon. Findings from this study underscore the need for additional support from mentors and other university entities to foster a stronger sense of resilience in students, along with increased opportunities for participation in research, academic writing, and publication. Full article

Objective: This study aims to evaluate the effectiveness of a combined rehabilitative program integrating Graded Motor Imagery (GMI) and Occupational Therapy in improving upper limb function and autonomy in individuals with acquired brain injuries (ABIs), including stroke and traumatic brain injury. Methods: Twelve patients (mean age of 56.4 years) underwent a six-week intervention combining GMI and Occupational Therapy. Outcome measures included the Disabilities of the Arm, Shoulder and Hand questionnaire (DASH), Jebsen Taylor Hand Function Test (JTHFT), Canadian Occupational Performance Measure (COPM), 12-Item Short Form Health Survey (SF-12), Numeric Rating Scale for pain (NRS), and Montreal Cognitive Assessment (MoCA). Assessments were conducted at baseline, post-treatment, and at 3- and 6-month follow-ups. Data were analyzed using the Wilcoxon signed-rank test. Results: Statistically significant improvements (p < 0.05) were found in upper limb function (DASH), occupational performance and satisfaction (COPM), and physical health status (SF-12 physical component). Specific gains in hand function—particularly in writing and eating—were detected using the JTHFT. No significant changes were observed in pain perception or mental health outcomes. Conclusions: The integration of GMI with Occupational Therapy appears to be a promising and well-tolerated intervention for enhancing motor function and daily life participation in individuals with ABI. Although the small sample limits generalizability, these preliminary findings support further investigation through larger, controlled studies. Full article

Effective assessment development requires collaboration between multidisciplinary team members, and the process is often time-intensive. This study illustrates a framework for integrating generative artificial intelligence (GenAI) as a collaborator in assessment design, rather than a fully automated tool. The context was the development of a 12-item multiple-choice test for social work interns in a school-based training program, guided by design-based research (DBR) principles. Using ChatGPT to generate draft items, psychometricians refined outputs through structured prompts and then convened a panel of five subject matter experts to evaluate content validity. Results showed that while most AI-assisted items were relevant, 75% required modification, with revisions focused on response option clarity, alignment with learning objectives, and item stems. These findings provide initial evidence that GenAI can serve as a productive collaborator in assessment development when embedded in a human-in-the-loop process, while underscoring the need for continued expert oversight and further validation research. Full article

Digital portfolios have become an essential assessment tool in project-based and student-centered learning environments. Unfortunately, students exert minimal effort in creating digital portfolios because they find the writing component unchallenging. This issue is concerning since existing research predominantly focuses on the use of pre-existing platforms for building digital portfolios. With this concern, there is an opportunity to explore more challenging approaches to digital portfolio creation. Consequently, this study employs a project-based learning (PBL) approach within a website design and development course, where 176 undergraduate students completed weekly coding tasks culminating in a self-coded digital portfolio. Using a one-group posttest-only research design, data were collected through a structured questionnaire that included demographic items and validated scales measuring learning effectiveness and ownership of learning. The survey was administered electronically after students submitted their digital portfolio projects. The results reveal that device ownership shows only weak associations with students’ perceptions, while internet connectivity and self-reported academic performance demonstrate stronger relationships with engagement and ownership of learning. Additionally, prior experience with digital portfolios positively influences students’ engagement, motivation, and ownership of learning. Implications of these findings are discussed for supporting the integration of digital portfolios into technical disciplines. Overall, this study contributes to the literature on PBL methodology, expands our understanding of digital portfolio integration, and underscores the significance of student-centered pedagogies. Full article

The present study offers a systematic review of the current state of research on lattice structures manufactured by additive technologies for biomedical applications, with the aim of identifying common patterns, such as the use of triply periodic minimal surfaces (TPMS) for bone scaffolds, as well as technological gaps and future research opportunities. Employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, the review process ensures methodological rigor and replicability across the identification, screening, eligibility, and inclusion phases. Additionally, PRISMA was tailored by prioritizing technical databases and engineering-specific inclusion criteria, thereby aligning the methodology with the scope of this field. In recent years, a substantial surge in interdisciplinary research has underscored the promise of architected porous structures in enhancing mechanical compatibility, fostering osseointegration, and facilitating personalized medicine. A growing body of literature has emerged that explores the optimization of geometric features to replicate the behavior of biological tissues, particularly bone. Additive manufacturing (AM) has played a pivotal role in enabling the fabrication of complex geometries that are otherwise unachievable by conventional methods. The applications of lattice structures range from permanent load-bearing implants, commonly manufactured through selective laser melting (SLM), to temporary scaffolds for tissue regeneration, often produced with extrusion-based processes such as fused filament fabrication (FFF) or direct ink writing (DIW). Notwithstanding these advances, challenges persist in areas such as long-term in vivo validation, standardization of mechanical and biological testing, such as ISO standards for fatigue testing, and integration into clinical workflows. Full article

With the rapid development of big data and artificial intelligence, the demand for memory has exploded. As a key data structure in modern databases and distributed storage systems, the Log-Structured Merge Tree (LSM-tree) has been widely employed (such as LevelDB, RocksDB, etc.) in systems based on key–value pairs due to its efficient writing performance. In LSM-tree-based KV stores, typically deployed on systems with DRAM-SSD storage, the KV items are first organized into MemTable as buffer for SSTables in main memory. When the buffer size exceeds the threshold, MemTable is flushed to the SSD and reorganized into an SSTable, which is then passed down level by level through compaction. However, the compaction degrades write performance and SSD endurance due to significant write amplification. To address this issue, recent proposals have mostly focused on redesigning the structure of LSM trees. We discover the prevalence of unchanged data blocks (UDBs) in the LSM-tree compaction process, i.e., UDBs are written back to SSD the same as they are read into memory, which induces extra write amplification and degrades I/O performance. In this paper, we propose a KV store design in SSD, called RemapCom, to exploit remapping on these UDBs. RemapCom first identifies UDBs with a lightweight state machine integrated into the compaction merge process. In order to increase the ratio of UDBs, RemapCom further designs a UDB retention method to further develop the benefit of remapping. Moreover, we implement a prototype of RemapCom on LevelDB by providing two primitives for the remapping. Compared to the state of the art, the evaluation results demonstrate that RemapCom can reduce write amplification by up to 53% and improve write throughput by up to 30%. Full article

Background: The prevention of pressure injuries depends on the early and correct assessment of at-risk patients. Since risk assessment involves more than using a risk factor instrument, we intend to map the existing recommendations and statements of good practice for pressure injury risk assessment in adults admitted to intensive care units, as well as identify the strengths of the evidence and recommendations in the literature. Methods: This study is a scoping review, guided by the Joanna Briggs Institute framework. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews was adopted as a guide for writing this study. Results: Searches were carried out in six databases, resulting in 794 studies, of which 15 were included. The recommendations and statements of good practice were grouped into five categories: risk assessment instruments, skin assessment, medical device surveillance, other alternatives to risk assessment, and implementing best practices in clinical settings. The strengths of the evidence and recommendations were identified when available in the literature. Conclusions: The mapping showed that the evidence is sufficient to indicate recommendations and statements of good practice for the risk assessment of pressure injuries in adults admitted to intensive care units. The protocol was retrospectively registered in the Open Science Framework on the 4th of August of 2023. Full article

Generative artificial intelligence (GenAI) tools developed to support teaching and learning are widely available. Trustworthiness concerns, however, have prompted calls for researchers to study their effectiveness and for educators and educational researchers to be involved in their creation and piloting processes. This study investigated one type of GenAI created to support educators: multiple-choice question generators (MCQ GenAI). Among the nine MCQ GenAI tools investigated, a variety of useful options were available, but only one indicated teacher involvement and none mentioned testing experts in development processes. MCQ GenAI-created items (n = 270) were coded based on MCQ quality item-writing guidelines. Results showed 80.00% of items (n = 216) violated at least one guideline, with 73.70% (n = 199) likely to produce major measurement error (should not use without revision), 6.30% (n = 17) likely to elicit minor measurement error (consider modifying), and 20.00% (n = 54) acceptable (usable as created). Implications suggest multidisciplinary teams are needed in educational GenAI tool development. Full article

Context: The systematic evaluation of a practitioner’s adherence to and competence in delivering psychotherapeutic interventions can be complex. This study describes the development of a fidelity assessment tool for the Reitman Centre CARERS Program (RCCP), a carer group-psychotherapy intervention with multiple didactic and clinical components. The tool’s value in informing psychotherapy training and best practices for practitioners from diverse professional settings is examined. Methods: The RCCP Fidelity Assessment Tool (RCCP-FAT) was developed following an iterative process of item writing and checking. Seven components of the RCCP—Group Structure, Dementia Education, Problem-Solving Techniques, Therapeutic Simulation, Vertical Cohesion, Horizontal Cohesion, and Overall Global Rating—were assessed, with three to eight items, and a “global score” assigned to each. Fifteen trained raters were paired up to rate 36 RCCP sessions using the RCCP-FAT. Rater agreement, correlation between itemized and global scores, and correlation between global ratings and RCCP participants’ satisfaction were calculated. Results: A total of 1188 RCCP-FAT items were rated by each of the two rater groups. Rater agreement was calculated to be 54.3% (κ = 0.32; 95% CI, 0.02681–0.3729). A positive correlation was found between the itemized and global scoring for four RCCP components evaluated (R = 0.833 to 0.929; p < 0.01). The global score and the participants’ satisfaction with “Simulation” was also positively correlated (R = 0.626, p < 0.01). Conclusions: The study provided evidence for fair rater agreement for all RCCP-FAT assessment items. More importantly, the process of developing the tool systematically crystallized the clinical elements of the RCCP and helped to standardize the training methods by creating a framework for providing feedback to learners that matches the items on the RCCP-FAT. The use of the RCCP-FAT to guide the training and mentoring of incoming group leaders is essential in the scaling and dissemination of a complex training method like the RCCP to ensure fidelity to the original evidence-based intervention. Full article

Objective: The objective was to develop and validate a multidimensional scale that measures adherence levels to antiseizure medications and detects patients’ reasons for non-adherence. Methods: A new scale was developed, namely the “Adherence Scale for Anti-Seizure Medication(s)-10 items [ASASM-10]”. It consists of ten statements that cover different causes of non-adherence to antiseizure medications. The domain selection was based on a comprehensive literature review. Guidelines for constructing an effective scale were followed to write the statements. Three independent expert judges assessed the content validity of the scale. The reliability of ASASM-10 was tested using three methods: internal consistency measurement (Cronbach’s alpha), Intraclass Correlation Coefficient [ICC] with a 95% Confidence Interval [95% CI], and test–retest reliability. Validity was tested using Principal Component Analysis [PCA] and a correlation coefficient. PCA was applied after measuring sampling adequacy via Kaiser–Meyer–Olkin [KMO] and Bartlett’s sphericity. The Medication Adherence Rating Scale [MARS] was selected as a pre-existing self-report method for validation of this new scale. Results: A total of 162 patients completed the study scales (mean ages ± SD: 34.07 ± 10.406 years). The scale demonstrated a good internal consistency with Cronbach’s alpha coefficient of 0.80 and exceeded the required value (i.e., 0.70) for the reliability of new scales. ASASM-10 showed a satisfactory ICC (95% CI) of 0.799 (0.718–0.857), p-value < 0.001. The test–retest reliability demonstrated a good correlation of ρ = 0.648, p-value < 0.001. The construct validity assessed by PCA retained four components as their eigenvalues exceeded one. The correlation coefficient demonstrated a positive moderate correlation between ASASM-10 and MARS (ρ = 0.283), p-value < 0.001. Conclusions: The present analyses provided evidence that ASASM-10 is a reliable and valid scale for evaluating patients’ adherence to antiseizure medications. It is the first available scale for assessing medication adherence in patients with epilepsy that can be utilized in clinical practice and research settings. Full article