Discrepancy Scores Research Articles

Turfgrass Management Professional Development Needs Assessment of Extension Agents

Turfgrass management includes many different components and without proper management turfgrass aesthetics will diminish. In addition, mismanagement of turfgrass systems could lead to negative environmental impacts. This situation creates the need for Extension agents to deliver turfgrass management educational and outreach programs to the various stakeholders and the general public. However, Extension agents require professional development in terms of turfgrass management. A needs assessment of [State] Extension agents was conducted at the University of Florida to determine the professional development needs relating to turfgrass management. Through this needs assessment, there were 51 individual competencies identified that were categorized into nine distinct competency domains. In general, the highest priority relative to professional development needs were related to the pest management (i.e., disease, insect, weeds, and nematodes) competency domains. Extension specialists can use the information from this needs assessment to adjust current and tailor new turfgrass management professional development programs to address the identified needs with the highest priority [i.e., greatest mean weighted discrepancy score (MWDS)]. Professional development programs could include creating new or adjusting educational materials and resources for the needs identified, in-service trainings for Extension agents to provide additional education, series of academies to provide baseline knowledge to Extension agents, and an online database to provide information and guidance. In addition, the results and needs identified from this needs assessment can be used as a basis for obtaining educational funding.

The effects of life review-based collage book making on psychosocial functioning and discrepancy between patients' and hospital staff's perception of daily functioning in long-term hospitalized patients with schizophrenia: A randomized controlled trial

The purpose of this study was to investigate the psychosocial effects of life review-based collage book making on long-term inpatients with schizophrenia, and to verify the effectiveness of this program in reducing the discrepancy between self-perception and others' evaluation of patients' day-to-day functioning. Participants were randomly assigned to intervention or control group. The intervention group participated in an individual program of life review-based collage book making. At three evaluation points (pre-and post-intervention and at 3-month follow-up) the patients rated their subjective difficulties, and the staff rated the objective severity of the patient's day-to-day functioning, using the Schizophrenia Cognition Rating Scale Japanese version (SCoRS-J). The Profile of Mood Status-Brief Form (POMS), Recovery Assessment Scale, and Life Skills Profile were used to assess psychosocial functioning, and the changes in scores on the scales were compared between the two groups. In addition, for those with large discrepancy between patient and staff rating scores on the SCoRS-J at baseline, the change in the discrepancy scores were compared between the two groups. As a result, there was a significant group main effect and interaction between the two groups in the changes in the Confusion subscale scores of the POMS, indicating an alleviation of confusion in this program. In addition, for those with large discrepancy scores on the SCoRS-J, there was a significant group main effect and interaction between the two groups in discrepancy scores, indicating that this program can contribute to the reduction of discrepancies in the perceptions, and to the collaborative approach to treatment.

Siamese Graph Convolutional Network quantifies increasing structure-function discrepancy over the cognitive decline continuum

Background and Objective:Alzheimer’s disease dementia (ADD) is well known to induce alterations in both structural and functional brain connectivity. However, reported changes in connectivity are mostly limited to global/local network features, which have poor specificity for diagnostic purposes. Following recent advances in machine learning, deep neural networks, particularly Graph Neural Network (GNN) based approaches, have found applications in brain research as well. The majority of existing applications of GNNs employ a single network (uni-modal or structure/function unified), despite the widely accepted view that there is a nontrivial interdependence between the brain’s structural connectivity and the neural activity patterns, which is hypothesized to be disrupted in ADD. This disruption is quantified as a discrepancy score by the proposed “structure-function discrepancy learning network” (sfDLN) and its distribution is studied over the spectrum of clinical cognitive decline. The measured discrepancy score is utilized as a diagnostic biomarker and is compared with state-of-the-art diagnostic classifiers. Methods:sfDLN is a GNN with a siamese architecture built on the hypothesis that the mismatch between structural and functional connectivity patterns increases over the cognitive decline spectrum, starting from subjective cognitive impairment (SCI), passing through a mid-stage mild cognitive impairment (MCI), and ending up with ADD. The structural brain connectome (sNET) built using diffusion MRI-based tractography and the novel, sparse (lean) functional brain connectome (ℓNET) built using fMRI are input to sfDLN. The siamese sfDLN is trained to extract connectome representations and a discrepancy (dissimilarity) score that complies with the proposed hypothesis and is blindly tested on an MCI group. Results:The sfDLN generated structure-function discrepancy scores show high disparity between ADD and SCI subjects. Leave-one-out experiments of SCI-ADD classification over a cohort of 42 subjects reach 88% accuracy, surpassing state-of-the-art GNN-based classifiers in the literature. Furthermore, a blind assessment over a cohort of 46 MCI subjects confirmed that it captures the intermediary character of the MCI group. GNNExplainer module employed to investigate the anatomical determinants of the observed discrepancy confirms that sfDLN attends to cortical regions neurologically relevant to ADD. Conclusion:In support of our hypothesis, the harmony between the structural and functional organization of the brain degrades with increasing cognitive decline. This discrepancy, shown to be rooted in brain regions neurologically relevant to ADD, can be quantified by sfDLN and outperforms state-of-the-art GNN-based ADD classification methods when used as a biomarker.

Effectiveness of large language models in automated evaluation of argumentative essays: finetuning vs. zero-shot prompting

To address the long-standing challenge facing traditional automated writing evaluation (AWE) systems in assessing higher-order thinking, this study built an AWE system for scoring argumentative essays by finetuning the GPT-3.5 Large Language Model. The system’s effectiveness was compared with that of the non-finetuned GPT-3.5 and GPT-4 base models via zero-shot prompting, which involves applying the model to perform tasks without any prior specific training or examples on those tasks. The dataset used was the TOEFL Public Writing Dataset provided by Education Testing Service (ETS), containing 480 argumentative essays with ground truth scores under two essay prompts. Three finetuned models were generated: two finetuned exclusively on either prompt and one on both. All finetuned and base models were used to score the remaining essays after finetuning and their scoring effectiveness was compared with ground truth scores, i.e., benchmark scores assigned by ETS-trained human raters. The impact of the variety of finetuning prompts and the robustness of finetuned models were also explored. Results showed a 100% consistency of all models in two scoring sessions. More importantly, the finetuned models significantly outperformed the base models in accuracy and reliability. The best-performing model, finetuned on prompt 1, showed an RMSE of 0.57, a percentage agreement (score discrepancy ≤ 0.5) of 84.72% and a QWK of 0.78. Further, the model finetuned on both prompts did not exhibit enhanced performance, and the two models finetuned on one prompt remained robust when scoring essays from the alternative prompt. These results suggest (1) task-specific finetuning for AWE is beneficial; (2) finetuning does not require a large variety of essay prompts; and (3) fine-tuned models are robust to unseen essays.

Assessing the Use of Radiographs Alone to Determine Air Force Dental Readiness Classification.

The Air Force Dental Service is responsible for ensuring that airmen are dentally ready to support military operations worldwide by delivering top-tier dental care. As the military healthcare landscape undergoes significant changes, the Air Force Dental Service has explored innovative approaches to dental care delivery. One consideration involves the potential use of radiographs as the primary tool for assessing service members' dental conditions, specifically focusing on identifying nondeployable conditions and periodontal health. Providers who previously participated as examiners in the 2018 Air Force Recruit Oral Health Study were recruited to re-evaluate randomly selected de-identified records, this time making assessments exclusively based on radiographs. Their evaluations included Dental Readiness Classification (DRC) determinations, total caries counts, and Periodontal Screening and Recording (PSR) index scores, providers also rated their confidence in these conclusions using a 5-point Likert scale. The study then computed sensitivity and specificity to assess the diagnostic performance of providers using radiographs only compared to the original study results that use the gold standard of radiographs with a clinical examination. Providers exceled at ruling out most DRC 3 conditions, with specificities surpassing 70%. Positively identifying those with DRC 3, particularly radiographically identifying periodontal conditions posed challenges with computed sensitivity rates as low as 8%. Discrepancies in PSR scores also accentuated limitations in relying solely on radiographs, where provider's radiographically determined PSR scores that matched less than one third of the time. In general, providers had low to very low confidence in their assessments. The study strongly cautions against relying solely on radiographs for determining the dental health of U.S. Air Force personnel. While providers effectively ruled out the absence of certain conditions, the challenge of positively identifying DRC 3 conditions poses significant risks to oral health if such a workflow was utilized. Particularly, the high probability of false negatives would be detrimental to the operational readiness of military personnel. Therefore, results support the continued use of radiographic and clinical examinations for comprehensive dental exams.

Cognitive Tracing Data Trails: Auditing Data Provenance in Discriminative Language Models Using Accumulated Discrepancy Score

Cognitive Tracing Data Trails: Auditing Data Provenance in Discriminative Language Models Using Accumulated Discrepancy Score

Analyzing Test Performance of BSIT Students and Question Quality: A Study on Item Difficulty Index and Item Discrimination Index for Test Question Improvement

This study presents a comprehensive assessment of the test performance of Bachelor of Science in Information Technology (BSIT) students in the System Integration and Architecture (SIA) course, coupled with a meticulous examination of the quality of test questions, aiming to lay the groundwork for enhancing the assessment tool. Employing a cross-sectional research design, the study involved 200 fourth-year students enrolled in the course. The results illuminated a significant discrepancy in scores between upper and lower student cohorts, highlighting the necessity for targeted interventions, curriculum enhancements, and assessment refinements, particularly for those in the lower-performing group. Further examination of the item difficulty index of the assessment tool unveiled the need to fine-tune certain items to better suit a broader spectrum of students. Nevertheless, the majority of items were deemed adequately aligned with their respective difficulty levels. Additionally, an analysis of the item discrimination index identified 25 items suitable for retention, while 27 items warranted revision, and 3 items were suitable for removal, as per the analysis outcomes. These insights provide a valuable foundation for improving the assessment tool, thereby optimizing its capacity to evaluate students' acquired knowledge effectively. The study's novel contribution lies in its integration of both student performance assessment and evaluation of assessment tool quality within the BSIT program, offering actionable insights for improving educational outcomes. By identifying challenges faced by BSIT students and proposing targeted interventions, curriculum enhancements, and assessment refinements, the research advances our understanding of effective assessment practices. Furthermore, the detailed analysis of item difficulty and discrimination indices offers practical guidance for enhancing the reliability and validity of assessment tools in the BSIT program. Overall, this research contributes to the existing body of knowledge by providing empirical evidence and actionable recommendations tailored to the needs of BSIT students, promoting educational quality and student success in Information Technology.

Resting-state networks and anosognosia in Alzheimer's disease.

Recent evidence suggests that anosognosia or unawareness of cognitive impairment in Alzheimer's Disease (AD) may be explained by a disconnection between brain regions involved in accessing and monitoring information regarding self and others. It has been demonstrated that AD patients with anosognosia have reduced connectivity within the default mode network (DMN) and that anosognosia in people with prodromal AD is positively associated with bilateral anterior cingulate cortex (ACC), suggesting a possible role of this region in mechanisms of awareness in the early phase of disease. We hypothesized that anosognosia in AD is associated with an imbalance between the activity of large-scale resting-state functional magnetic resonance imaging (fMRI) networks, in particular the DMN, the salience network (SN), and the frontoparietal network (FPN). Sixty patients with MCI and AD dementia underwent fMRI and neuropsychological assessment including the Anosognosia Questionnaire Dementia (AQ-D), a measure of anosognosia based on a discrepancy score between patient's and carer's judgments. After having applied Independent Component Analysis (ICA) to resting fMRI data we performed: (i) correlations between the AQ-D score and functional connectivity in the DMN, SN, and FPN, and (ii) comparisons between aware and unaware patients of the DMN, SN, and FPN functional connectivity. We found that anosognosia was associated with (i) weak functional connectivity within the DMN, in posterior and middle cingulate cortex particularly, (ii) strong functional connectivity within the SN in ACC, and between the SN and basal ganglia, and (iii) a heterogenous effect concerning the functional connectivity of the FPN, with a weak connectivity between the FPN and PCC, and a strong connectivity between the FPN and ACC. The observed effects were controlled for differences in severity of cognitive impairment and age. Anosognosia in the AD continuum is associated with a dysregulation of the functional connectivity of three large-scale networks, namely the DMN, SN, and FPN.

Can ChatGPT provide accurate financial toxicity resources to patients with cancer?

e13638 Background: Artificial Intelligence (AI) has potential benefits in medicine, including improving patient education, but its use in mitigating financial toxicity faced by patients with cancer is unknown. We aimed to investigate the accuracy of ChatGPT, an open access AI, responses to common financial concerns among patients with cancer. Methods: ChatGPT, an open access AI, was used to query 50 questions developed using oncologic society websites. These questions encompassed medical (n=25) and non-medical financial (n=25) inquiries related to breast, endometrial, lung, colon, and prostate cancers. The answers were scored by cancer financial toxicity experts with the scale: 1) correct and comprehensive, 2) correct but not comprehensive, 3) some correct, some incorrect components and 4) completely incorrect. Score discrepancies were resolved with a third reviewer. In secondary analysis, all questions were queried 3 times to assess if ChatGPT could provide more comprehensive answers on subsequent inquiries. The proportion of responses earning each score were calculated overall and within each question category and disease site. Results: Overall, in the first queries, 84% of questions were scored as correct but not comprehensive (n=42/50) and 6% (n=3/50) of questions were scored as having both correct and incorrect components. Approximately 28% (n=14/50) of questions had discrepant scores that required third party review, and 10% (n=5/50) of questions never achieved a consensus. All non-medical questions were scored as correct but not comprehensive (n=25/25) with a concordance rate of 96% (n=24/25), with only 1 question sent to a third reviewer. Additional queries of non-medical questions did not yield different answers. For the medical questions, approximately 68% (n=17/25) of questions were scored as correct but not comprehensive and 12% (n=3/25) were scored as having correct and incorrect components. 52% (n=13/25) of questions were sent to a third reviewer, and 20% (n=5/25) of questions never achieved a consensus score. While additional queries of medical questions yielded different answers, the quality of the responses did not improve. For each disease site, prostate cancer questions had the highest number of discordant questions with 40% (n=4/10) that were sent to a third reviewer for scoring but a 100% (n=10/10) of responses had a score that showed correct but not comprehensive answers. Reviewer feedback was universal that ChatGPT responses did not provide helpful information to patients, even in the situations in which there was technically no inaccurate information. Conclusions: ChatGPT does not consistently provide accurate or helpful answers to financial toxicity concerns commonly asked by patients with cancer. Additional input from experts is necessary to aid in AI learning before this resource should be used by patients facing financial toxicity.

Large language foundation models encode clinical radiation oncology domain knowledge: Performance on the American College of Radiology Standardized Examination.

e13585 Background: The integration of Large Language Models (LLMs) into healthcare and medical education will represent a significant paradigm shift, offering transformative potential in how medical knowledge is accessed and assimilated. These models, however, have not yet been systematically trained, tested, or validated on complex medical information such as sub-specialty medical examinations. This study explores the performance of seven major LLMs in clinical radiation oncology using residency in-training exams. Methods: In this study, the 2021 American College of Radiology (ACR) Radiation Oncology In-Training Exam (TXIT) was used to evaluate the performance of various LLMs, including OpenAI's GPT-3.5-turbo, GPT-4, GPT-4-turbo, three Meta's Llama-2 models (7 billion, 13 billion, and 70 billion parameter), and Google's PaLM-2-text-bison. The ACR provided the publicly available national scoring for this exam. The exam comprised 298 questions across 13 domains, including clinical radiation oncology (195 questions, 65.4%). The exam was processed through each LLM via an application programming interface. LLM-generated answers were analyzed by clinical disease sites and compared to Radiation Oncology trainee performance and stratified by Post-Graduate Year (PGY) 2-5. Results: LLMs showed varied performance in the overall clinical radiation oncology domain, with OpenAI's GPT-4-turbo having the best performance with 68.0% correct answers, GPT-4 61.0%, GPT-3.5-turbo 48.0%, PaLM-2-text-bison 40.0%, and then the three Llama-2 models (70b 37.0%, 13b 38%, 7b 26%). GPT-4-turbo performed superiorly to lower-level (PGY2 51.6%, PGY3 61.6%) and comparably to upper-level radiation oncology trainees (PGY4 64.1%, PGY5 68.3%). Notably, GPT-4-turbo demonstrated 7.0% improvement over its predecessor GPT-4. LLMs scored the lowest in the gastrointestinal, genitourinary, and gynecology domains and highest in the bone and soft tissue, central nervous system and eye, and head, neck, and skin domains. Conclusions: GPT-4-turbo demonstrates clinical accuracy comparable to upper-level and superior to lower-level trainees in nearly all clinical domains. Conversely, Llama2 foundation models demonstrate overall worse performance than Level 1 (PGY2) trainees. Score discrepancies across disease site domains may be due to data availability, complexity of medical conditions, quality and quantity of training datasets, and interdisciplinary data inputs. Future research will need to evaluate the performance of models that are fine-tune trained in clinical oncology. This study also underscores the need for rigorous validation of LLM-generated information against established medical literature and expert consensus, necessitating expert oversight in their application in medical education and practice.

Systematic review and meta-analysis of single-stage versus two-stage revision for periprosthetic joint infection after knee arthroplasty: a call for a randomised trial.

Knee arthroplasty is an effective treatment for severe knee degeneration; however, periprosthetic joint infection (PJI) is one of its serious complications. Single- and two-stage revision are common treatments, but few studies have compared single- and two-stage revision for PJI after knee arthroplasty. This study aimed to compare the reinfection and reoperation rates of single- and two-stage revision through meta-analysis. The review process was conducted according to the PRISMA guidelines. We searched the PubMed, Medline, Embase and Cochrane Central Register of Controlled Trials databases for trials comparing single- and two-stage revision for PJI after knee arthroplasty from the respective inception dates to April 2023. Two researchers individually screened the studies, performed the literature quality evaluation and data extraction and used Stata 17 software for data analysis. The meta-analysis showed that the reinfection rate was significantly lower in the single-stage revision group than in the two-stage revision group. While the reoperation rates demonstrated no statistically significant difference between the two groups. We presented descriptive results because the discrepancies in the knee function scores and data reported in the studies meant that these data could not be combined in the meta-analysis. Based on the available research, single-stage revision is a reliable option for PJI after knee arthroplasty. However, when developing the best treatment strategy, it is still necessary to consider the individual circumstances and needs of the patient, as well as the risks of postoperative rehabilitation and complications.

The Development Strategy of The Civil Apparatus of Pemalang Regency Through the Bureaucratic Evaluation Information System (Serabi)

Purpose: The purpose of this study was to evaluate the implementation and effectiveness of the SERABI (Bureaucratic Reform Evaluation System Application) in monitoring and evaluating the state civil apparatus in Pemalang Regency, and to identify any obstacles encountered in its use. Theoretical reference: The study is grounded in the theory of e-government and its role in enhancing public administration through technological innovations, specifically in the context of bureaucratic reform and public service delivery. Method: The research employed a qualitative method with a descriptive approach to assess the implementation of the SERABI application. Data were collected through direct observation, interviews, and document analysis related to the usage of the SERABI system by various Organizational Units (OPDs) within the Pemalang Regency Government. Results and Conclusion: The study found that the implementation of the SERABI application in Pemalang Regency has generally been successful, with most OPDs completing self-assessments and inputting the required data into the system. However, there were discrepancies in the evaluation scores for three OPDs, which received a grade of D, indicating room for improvement. The results suggest that while the SERABI application has been effective in promoting compliance and facilitating bureaucratic reform, certain challenges remain, particularly in ensuring the accuracy and consistency of the evaluations. Implications of research: The findings imply that while e-government applications like SERABI can significantly improve the efficiency and transparency of bureaucratic processes, continuous monitoring, training, and support are necessary to address implementation challenges and improve system accuracy. Policymakers and administrators can use these insights to refine their strategies for deploying similar e-government solutions. Originality/value: This study provides valuable insights into the practical application of an e-government innovation in a regional setting. By focusing on the SERABI application, it highlights the specific challenges and successes associated with using technology to drive bureaucratic reform, offering a model for other regions looking to implement similar initiatives.

Resolving and Re-Scoring Constructed Response Items in Mixed-Format Assessments: An Exploration of Three Approaches

ABSTRACT We explored three approaches to resolving or re-scoring constructed-response items in mixed-format assessments: rater agreement, person fit, and targeted double scoring (TDS). We used a simulation study to consider how the three approaches impact the psychometric properties of student achievement estimates, with an emphasis on person fit. We found that although TDS is not specifically designed to identify discrepant or misfitting score profiles, this approach can improve person fit for students who are also within a critical range of achievement. Our findings also suggest that TDS alone is not sufficient to provide a comprehensive picture of measurement quality because it does not directly consider person fit. We suggest that researchers and practitioners consider combining TDS with other approaches to score resolution. We consider the implications of our findings for research and practice.

Shifting goalposts: widening discrepancies between girls' actual and ideal bodies predict disordered eating from preadolescence to adulthood.

Little is known regarding how disordered eating (DE) relates to perceived actual body size, ideal body size, and their discrepancy. This study examined changes in perceived actual body size, ideal body size, and actual-ideal discrepancies over time, and their relationship with subsequent DE. Participants were 759 female twins from the Minnesota Twin Family Study who reported on body image and DE every three to five years between approximately ages 11 to 29. We used multilevel modeling to examine developmental trajectories of body mass index (BMI) and Body Rating Scale Actual, Ideal, and Actual-Ideal discrepancy scores and compared the degree to which BMI, BRS body size perceptions, and body dissatisfaction predicted DE behaviors and attitudes over time. Participants were treated as singletons in analyses. Perceived Actual body sizes and BMIs increased from age 10 to 33, whereas Ideal body sizes remained largely stable across time, resulting in growing Actual-Ideal discrepancies. Body size perceptions and Actual-Ideal discrepancies predicted subsequent DE behaviors and attitudes more strongly than did body dissatisfaction as measured by self-report questionnaires. This research advances understanding of how female body size perceptions and ideals change across development and highlights their relationship with subsequent DE.

Socratic guided feedback therapy after acquired brain injury: A multicenter randomized controlled trial to evaluate effects on self-awareness

ABSTRACT Impaired self-awareness after acquired brain injury (ABI) challenges neuropsychological rehabilitation. The current study aimed to compare the effects of Socratic Guided Feedback therapy to usual care in a multicenter randomized controlled trial with 64 participants with reduced self-awareness after ABI. The objectives were to study the effects on (1) self-awareness and (2) motivation for and participation in therapy, mood, quality of life, and social participation. Patients were recruited from rehabilitation centres in The Netherlands and Belgium. They were 50.8 (±16) years old and 2.7 months (±1.8) post-injury at baseline. Session duration ranged from 20–60 minutes and the number of sessions ranged from 1 to 162 sessions. Self-awareness increased over time in both groups. Between 9 and 12 months after baseline measurement, self-awareness (Patient Competency Rating Scale discrepancy score between patient and significant other) improved in the experimental group and deteriorated in care as usual. No significant differences were found on secondary outcome measures. In conclusion, Socratic Guided Feedback therapy is as effective as care as usual but provides a structure for therapists. We recommend further investigation of the added value of Socratic Guided Feedback therapy in later follow-up measurements, group therapy settings, and on other outcome domains such as caregiver burden.

Covariate-adaptive randomization inference in matched designs

Abstract It is common to conduct causal inference in matched observational studies by proceeding as though treatment assignments within matched sets are assigned uniformly at random and using this distribution as the basis for inference. This approach ignores observed discrepancies in matched sets that may be consequential for the distribution of treatment, which are succinctly captured by within-set differences in the propensity score. We address this problem via covariate-adaptive randomization inference, which modifies the permutation probabilities to vary with estimated propensity score discrepancies and avoids requirements to exclude matched pairs or model an outcome variable. We show that the test achieves type I error control arbitrarily close to the nominal level when large samples are available for propensity score estimation. We characterize the large-sample behaviour of the new randomization test for a difference-in-means estimator of a constant additive effect. We also show that existing methods of sensitivity analysis generalize effectively to covariate-adaptive randomization inference. Finally, we evaluate the empirical value of combining matching and covariate-adaptive randomization procedures using simulations and analyses of genetic damage among welders and right-heart catheterization in surgical patients.

Beyond Difference Scores: Unlocking Insights with Polynomial Regression in Studies on the Effects of Implicit-Explicit Congruency.

The aim of our study was to investigate whether theories of congruence are better tested using polynomial regression analysis, rather than expressing discrepancy between implicit and explicit measures as continuous or categorical difference scores. This paper also aims to make knowledge more accessible by providing a step-by-step explanation of both methods, illustrating differences between them, and making materials openly available for other researchers. In this paper, implicit and explicit measures of self-esteem are used as predictors for depressive symptoms, anxiety, and aggression in a general population sample (N = 135). Explicit self-esteem was measured using the Rosenberg Self-Esteem Scale, implicit self-esteem was measured using the Implicit Association Test, and the Symptom Questionnaire was used to measure depressive symptoms, anxiety, and aggression. The results show those difference score models all imply that the discrepancy between implicit and explicit self-esteem explains depression and anxiety, but not aggression. However, polynomial regression analysis shows that depression and anxiety are not accounted for by the explicit-implicit discrepancy as such, but are foremost explained by explicit self-esteem. Polynomial regression has the potential to evaluate more complex and more detailed hypotheses than what would be possible using statistical approaches based on discrepancy scores. It is therefore recommended for future research aimed at disentangling the roles of explicit and implicit self-esteem in psychological outcomes.

Awareness and cognitive rehabilitation in Alzheimer's disease and frontotemporal dementia.

Awareness influences the evolution of neurodegenerative dementias. We gathered participants' and caregivers assessments of dependence in daily activities and we studied how each score would be related to next year participant autonomy, independently of other explicative variables. We retrospectively analyzed data from mildly demented participants with a clinical diagnosis of Alzheimer's disease (AD, n = 186) and frontotemporal dementia (FTD, n = 29) and their relatives. A research tool was used to assess participant dependence in 98 daily activities and associated caregiver burden. A discrepancy score between the patient's and relative's judgment was calculated to evaluate awareness of dependence in activities at baseline. This dependence scores, as well as sex, age, education, and 1 year difference in Mini-Mental State Examination were taken as possible explicative variables for dependence in activities adapted by therapists during a 1-year cognitive rehabilitation program. Patients with FTD showed less awareness for daily dependence (discrepancy 20.9% vs. 11.8% in AD). Both groups benefited from cognitive rehabilitation (25% decrease in dependence) and subjective burden of relatives was decreased in both groups. In the AD group, there was a significant positive relationship between both caregiver (P < 0.001) and participant's (P < 0.02) evaluation of dependence in daily activities at inclusion and dependence of participants in adapted activities after 1 year. Awareness of impairment in daily activities is a clinical symptom that is more important at inclusion in FTD than in AD. However, in participants with AD who, as a group, significantly benefit from a cognitive rehabilitation program, not only caregiver's but also participant's assessment of dependence at baseline is correlated to subsequent, next year greater dependence in daily activities adapted by the therapists. Although discrepant, both caregiver and participant evaluations appear to be important variables to understand the evolution and the benefit of care in participants at early stages of dementia.

Functional needs in orthognathic patients with different sagittal skeletal discrepancies

ObjectivesThis retrospective study aimed to evaluate the relationship between the Index of Orthognathic Functional Treatment Need (IOFTN) and sagittal skeletal discrepancy (ANB angle) among orthognathic patients. Study DesignRecords of 403 orthognathic patients (229 females/174 males, aged 16-50 years) were reviewed. Malocclusion type (incisor-based), sagittal skeletal relationship (ANB angle), and IOFTN scores were documented. Spearman correlation coefficient (r) and scatter plots were utilized to examine the relationship between functional need (IOFTN scores) and sagittal skeletal discrepancy (ANB angle). Regression analyses were conducted to determine the extent to which variations in IOFTN scores could be attributed to variations in ANB angles. ResultsClass III malocclusion/sagittal skeletal pattern were most prevalent (62.3%). The Spearman correlation coefficient (r) for the entire sample was -0.297 (P<0.001), indicating a weak negative correlation. Linear regression analysis demonstrated a coefficient of determination of 0.069, indicating that approximately 6.9% of the variance in IOFTN data could be accounted for by variations in ANB angles. Upon stratifying data by different malocclusions, coefficients of determination were 0.065, 0.034, and 0.089 for Class I, II, and Class III malocclusions, respectively. ConclusionThe observed linear relationships between sagittal skeletal discrepancy (ANB angle) and IOFTN score were weak and lacked clinical significance.

Appraising competency gaps among UNESCO-designated heritage site actors in disaster risk reduction innovations

Communities residing in UNESCO-designated sites, characterised by outstanding universal values, face heightened vulnerability during disasters, necessitating innovative Disaster Risk Reduction (DRR) strategies. The critical role of well-equipped UNESCO site actors in DRR necessary for achieving the goals of broader international frameworks such as Agenda 2030 and the Sendai Framework for Disaster Risk Reduction (SFDRR) warrants this needs assessment study. This study uses a descriptive survey design to assess the competency gaps of UNESCO site actors, including managers and staff, for effective utilisation of DRR innovations (DRRI). Employing the Borich Needs Assessment Model and Ranked Discrepancy Model, an online questionnaire garnered 141 responses from 59 countries. Descriptive statistics, significance tests (at p < .05), correlation tests, Mean Weighted Discrepancy Scores, and Ranked Discrepancy Scores yielded noteworthy findings. Despite recognising the importance of DRRI, respondents demonstrated competence lags, emphasizing the need for tailored training programs. Competency gaps were identified across all 14 listed DRRI, with top priorities including disaster prevention radio, telemetry systems, unmanned aerial vehicles, GIS, remote sensing, resilient materials, and disaster risk insurance. Enhancing competencies in DRRI presents a strategic approach to bolstering disaster preparedness, management, and risk reduction efforts, aligning with global conservation and sustainability goals.