Inflammatory bowel disease patients' perspectives of non-medical needs.

BACKGROUND: Inflammatory bowel disease (IBD) imposes a huge burden on the healthcare systems and greatly declines the patient's quality of life. However, there is a paucity of detailed data regarding information and supportive needs as well as sources and methods of obtaining information to control different aspects of the disease from the perspectives of the patients themselves. This study aimed to establish the IBD patients' preferences of informational and supportive needs through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). METHODS: IBD patients were recruited from different centers. Considering inclusion and exclusion criteria, 521 participants were filled a predefined questionnaire. This questionnaire was prepared through literature review of the recent well-known guidelines on the needs of IBD patients, which was further approved by the experts of IBD area in three rounds of Delphi consensus. It includes 56 items in four sections of informational needs (25), supportive needs (15), sources of information (7), and methods of obtaining information (9). RESULTS: In particular, EFA was used to apply data reduction and structure detection. Given that this study tries to identify patterns, structures as well as inter-relationships and classification of the variables, EFA was utilized to simplify presentation of the variables in a way that large amounts of observations transform into fewer ones. Accordingly, the EFA identified five factors out of 25 items in the information needs section, three factors out of 15 items in the supportive needs section, two factors out of 7 items in the information sources section, and two factors out of 9 items in the information presentation methods. Through the CFA, all 4 models were supported by Root Mean Squared Error of Approximation (RMSEA); Incremental Fit Index (IFI); Comparative Fit Index (CFI); Tucker-Lewis Index (TLI); and SRMR. These values were within acceptable ranges, indicating that the twelve factors achieved from EFA were validated. CONCLUSIONS: This study introduced a reliable 12-factor model as an efficient tool to comprehensively identify preferences of IBD patients in informational and supportive needs along with sources and methods of obtaining information. An in-depth understanding of the needs of IBD patients facilitates informing and supporting health service provision. It also assists patients in a fundamental way to improve adaptation and increase the quality of life. We suggest that health care providers consider the use of this tool in clinical settings in order to precisely assess its efficacy.

The Patient Activation Measure-13 (PAM-13) in an oncology patient population: psychometric properties and dimensionality evaluation.

BACKGROUND: Accurate assessment and enhancement of health-related skills among oncology patients are pivotal for optimizing cancer care. The Patient Activation Measure (PAM-13), a questionnaire designed to reflect an individual's knowledge, skills, and confidence in self-healthcare management, has been validated across diverse countries and settings. Concerns have been raised regarding the cross-situational applicability, as patients with specific diseases and cultural backgrounds interpret questionnaire items differently. This study aimed to examine the structural validity and psychometric properties of the PAM-13 in an oncological patient cohort. METHODS: Baseline data from a longitudinal non-randomized controlled study involving cancer out-patients (n = 1,125) from Comprehensive Cancer Centres in Southern Germany were analysed. The German version of the PAM-13 was employed. With classical test and item response theory methods data quality, reliability, convergent and structural validity, as well as psychometric properties were assessed. Exploratory (EFA) and confirmatory factor analyses (CFA) were employed to investigate the postulated unidimensionality of the underlying construct. With a partial credit model (PCM) we examined item fit, targeting, local independence and differential item functioning. RESULTS: Participants were predominantly female (73.0%) with a breast cancer diagnosis (41.3%). While items were generally well-accepted, ceiling effects were observed and a high mean PAM-13 score (69.7, SD = 14.2) was noted, potentially compromising responsiveness to interventions. Reliability was adequate (Cronbach's α = 0.81), person and item separation reliability were good to excellent (0.81 and 0.99, respectively). Explorations of the unidimensionality of the construct (EFA, CFA, PCM) yielded inconclusive results, hinting towards a two-factor solution. Item difficulty rankings deviated from the original. No differential item functioning was identified, and local independence was confirmed. CONCLUSIONS: While the PAM-13 serves as a valuable instrument for comprehending and promoting health-related skills in cancer patients, the identification of ceiling effects, disordered item-difficulty rankings, and inconclusive findings regarding unidimensionality contribute to the expanding body of evidence, emphasizing the dependency of PAM-13's validity and reliability on distinctive characteristics within the population under investigation. Future research should prioritize refining or adding PAM-13 items to better capture the specific health-related challenges within diverse populations, paving the way for more effective patient engagement strategies in oncology. TRIAL REGISTRATION NUMBER: DRKS00021779.

Associations between urine glyphosate levels and metabolic health risks: insights from a large cross-sectional population-based study.

BACKGROUND: The prevalence of metabolic syndrome (MetS) in American adults increased from 37.6% in the 2011-12 period to 41.8% in 2017-2018. Environmental exposure, particularly to common compounds such as glyphosate, has drawn increasing attention as a potential risk factor. METHODS: We employed three cycles of data (2013-2018) from the National Health and Nutrition Examination Survey (NHANES) in a cross-sectional study to examine potential associations between urine glyphosate measurements and MetS incidence. We first created a MetS score using exploratory factor analysis (EFA) of the International Diabetes Federation (IDF) criteria for MetS, with data drawn from the 2013-2018 NHANES cycles, and validated this score independently on an additional associated metric, the albumin-to-creatinine (ACR) ratio. The score was validated via a machine learning approach in predicting the ACR score via binary classification and then used in multivariable regression to test the association between quartile-categorized glyphosate exposure and the MetS score. RESULTS: In adjusted multivariable regressions, regressions between quartile-categorized glyphosate exposure and MetS score showed a significant inverted U-shaped or saturating dose‒response profile, often with the largest effect for exposures in quartile 3. Exploration of potential effect modification by sex, race, and age category revealed significant differences by race and age, with older people (aged > 65 years) and non-Hispanic African American participants showing larger effect sizes for all exposure quartiles. CONCLUSIONS: We found that urinary glyphosate concentration is significantly associated with a statistical score designed to predict MetS status and that dose-response coefficient is nonlinear, with advanced age and non-Hispanic African American, Mexican American and other Hispanic participants exhibiting greater effect sizes.

A Preliminary Exploratory Factor Analysis of the BDSM Counselor Competency Scale.

BDSM practitioners represent a large sexual minority group often overlooked, misunderstood, and unnecessarily pathologized by mental health clinicians. Although developing cultural competence for diverse and marginalized populations is widely understood to be a core component of delivering efficacious therapeutic services that can counteract these stigmatizing mental healthcare experiences, no measures currently exist that assess clinicians' self-reported competence to work with BDSM practitioners. Previous measurement work has been done to establish self-report competency scales for clinicians working with other sexual and gender minority groups, but no such scales exist for working with BDSM practitioners. In the current study, we adapted a version of the Sexual Orientation Counselor Competency Scale (SOCCS) to measure clinicians' self-reported competence to work with BDSM practitioners and did a preliminary exploratory factor analysis of the new scale (n = 124). After an initial 24-item administration, principal axis factoring of our final 17-item solution revealed two latent factors (attitudes and skills/knowledge) consistent with the 2013 SOCCS and the theoretical constructs of cultural competency. The BDSM Counselor Competency Scale (BDSM-CCS) can help clinicians, practices, agencies, and training programs track self-reported cultural competence with the BDSM population. Future research directions for scale development and clinical and training applications are discussed.

Salivary, lacrimal and nasal (SALANS) measure to assess side effects following radioactive iodine treatment: development, psychometric properties, and factor structure.

PURPOSE: This study aimed to develop and psychometrically evaluate a patient-reported outcome measure (PROM), SAlivary, LAcrimal, NaSal (SALANS), to document patients' symptoms after radioactive iodine (RAI) treatment for differentiated thyroid cancer (DTC). METHODS: We generated and iteratively revised SALANS items based on expert input, focus group discussions and feedback from cognitive testing (n = 17). We administered an initial SALANS measure with 39 items to patients diagnosed with DTC in the past two years (n = 105). Exploratory factor analysis (EFA) examined the factor structure of the SALANS items. We assessed the consistency reliability and related the total and subscale scores of the final SALANS to existing PROMs to assess validity. RESULTS: The final SALANS consisted of 33 items and six subscales (sialadenitis, taste, xerostomia, dry eyes, epiphora, and nasal) with six factors extracted by EFA. The six subscales demonstrated good internal reliability (α range = 0.87-0.92). The SALANS total score showed good convergent validity with the Xerostomia Inventory (r = 0.86) and good discriminant validity with a measure of spirituality (r = - 0.05). The mean SALANS total score was significantly higher (d = 0.5, p < 0.04) among patients who had RAI compared to those who did not have RAI. CONCLUSION: Preliminary evidence suggests that SALANS is a novel and reliable PROM to assess the type and frequency all symptoms experienced after RAI treatment for DTC. Future work is needed to further validate and develop the scale.

Factor structure and reliability of the symptom measurement of post-stroke depression in the rehabilitation stage.

BACKGROUND: The incidence of Post Stroke Depression (PSD) in the Rehabilitation Stage is high, which can bring serious physical and psychological disorders to patients. However, there is still a lack of targeted tools for screening PSD in the rehabilitation stage. Therefore, the aim of this study was to evaluate the factor structure and reliability of a measurement instrument to screen for PSD in the rehabilitation stage. METHODS: A cross-sectional study was conducted on 780 hospitalized stroke patients who were within the rehabilitation stage from May to August 2020. Exploratory factor analysis (EFA) as well as first- and second-order confirmatory factor analysis (CFA) were performed to evaluate the factor structure of the newly developed Symptom Measurement of Post-Stroke Depression in the Rehabilitation Stage (SMPSD-RS). The reliability and validity of the SMPSD-RS were also verified using several statistical methods. RESULTS: EFA extracted a 24-item, five-factor (cognition, sleep, behavior, emotion, and obsession) model that can clinically explain the symptoms of PSD during the rehabilitation stage. A first-order CFA confirmed the EFA model with good model fit indices, and the second-order CFA further confirmed the five-factor structure model and showed acceptable model fit indices. Acceptable reliability and validity were also achieved by the corresponding indicators. CONCLUSION: The SMPSD-RS was proven to have a stable factor structure and was confirmed to be reliable and valid for assessing PSD symptoms in stroke patients during the rehabilitation stage.

Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach.

BACKGROUND: The Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for identifying the risk of insomnia disorder. Although the ISI is still short, more shortened versions are emerging for repeated monitoring in routine clinical settings. In this study, we aimed to develop a data-driven shortened version of the ISI that accurately predicts the severity level of insomnia disorder. METHODS: We collected a sample of 800 responses from the EMBRAIN survey system. Based on the responses, seven items were grouped based on the similarity of their response using exploratory factor analysis (EFA). The most representative item within each group was selected by using eXtreme Gradient Boosting (XGBoost). RESULTS: Based on the selected three key items, maintenance of sleep, interference with daily function, and concerns about sleep problems, we developed a data-driven shortened questionnaire of ISI, ISI-3 m (machine learning). ISI-3 m achieved the highest coefficient of determination ( R 2 = 0.910 ) for the ISI score prediction task and the accuracy of 0.965, precision of 0.841, and recall of 0.838 for the multiclass-classification task, outperforming four previous versions of the shortened ISI. CONCLUSION: As ISI-3 m is a highly accurate shortened version of the ISI, it allows clinicians to efficiently screen for insomnia and observe variations in the condition throughout the treatment process. Furthermore, the framework based on the combination of EFA and XGBoost developed in this study can be utilized to develop data-driven shortened versions of the other questionnaires.

Stability and consistency of symptom clusters in younger versus older patients receiving chemotherapy.

BACKGROUND: By 2035, the number of newly diagnosed cancer cases will double and over 50% will be in older adults. Given this rapidly growing demographic, a need exists to understand how age influences oncology patients' symptom burden. The study purposes were to evaluate for differences in the occurrence, severity, and distress of 38 symptoms in younger (< 60 years) versus older (≥ 60 years) oncology patients undergoing chemotherapy and to evaluate for differences in the stability and consistency of symptom clusters across the two age groups. METHODS: A total of 1329 patients were dichotomized into the younger and older groups. Patients completed demographic and clinical questionnaires prior to the initiation of their second or third cycle of chemotherapy. A modified version of Memorial Symptom Assessment Scale was used to evaluate the occurrence, severity, and distress of 38 common symptoms associated with cancer and its treatment. Differences between the two age groups in demographic and clinical characteristics and ratings of occurrence, severity, and distress for the 38 symptoms were evaluated using parametric and nonparametric tests. Exploratory factor analyses were done within each age group to identify symptom clusters using symptom occurrence rates. RESULTS: Compared to the younger group (14.8 (± 7.0)), older adults reported a lower mean number of symptoms (12.9 (± 7.2)). Older patients experienced lower occurrence rates for almost 50% of the symptoms. Regarding symptom clusters, an eight-factor solution was selected for both age groups. Across the two age groups, the eight symptom clusters (i.e., physical and cognitive fatigue, respiratory, psychological, hormonal, chemotherapy-related toxicity, weight gain, gastrointestinal, epithelial) were stable. However, symptoms within the physical and cognitive, chemotherapy-related toxicity, and gastrointestinal clusters were not consistent across the age groups. CONCLUSIONS: To be able to provide tailored and effective symptom management interventions to older oncology patients, routine assessments of the core symptoms unique to the symptom clusters identified for this group warrants consideration. The underlying mechanism(s) for these inconsistencies in symptom burden is an important focus for future studies.

A comprehensive multivariate analysis of the center of pressure during quiet standing in patients with Parkinson's disease.

BACKGROUND: We hypothesized that postural instability observed in individuals with Parkinson's disease (PD) can be classified as distinct subtypes based on comprehensive analyses of various evaluated parameters obtained from time-series of center of pressure (CoP) data during quiet standing. The aim of this study was to characterize the postural control patterns in PD patients by performing an exploratory factor analysis and subsequent cluster analysis using CoP time-series data during quiet standing. METHODS: 127 PD patients, 47 aged 65 years or older healthy older adults, and 71 healthy young adults participated in this study. Subjects maintain quiet standing for 30 s on a force platform and 23 variables were calculated from the measured CoP time-series data. Exploratory factor analysis and cluster analysis with a Gaussian mixture model using factors were performed on each variable to classify subgroups based on differences in characteristics of postural instability in PD. RESULTS: The factor analysis identified five factors (magnitude of sway, medio-lateral frequency, anterio-posterior frequency, component of high frequency, and closed-loop control). Based on the five extracted factors, six distinct subtypes were identified, which can be considered as subtypes of distinct manifestations of postural disorders in PD patients. Factor loading scores for the clinical classifications (younger, older, and PD severity) overlapped, but the cluster classification scores were clearly separated. CONCLUSIONS: The cluster categorization clearly identifies symptom-dependent differences in the characteristics of the CoP, suggesting that the detected clusters can be regarded as subtypes of distinct manifestations of postural disorders in patients with PD.

Pre-hospital symptom clusters and symptom network analysis in decompensated cirrhotic patients: A cross-sectional study.

AIMS: To generate pre-hospital symptom networks, explore core, bridge and sentinel symptoms, identify pre-hospital symptom clusters and analyse relationship between influencing factors and symptom clusters in decompensated cirrhosis patients. DESIGN: A cross-sectional study design using the Strengthening the Reporting of Observational Studies in Epidemiology checklist. METHODS: Demographical, physiological, psychological and sociological characteristics and the pre-hospital symptoms of 292 decompensated cirrhotic patients were collected from October 2021 to March 2023 in China. Frequencies, percentages, means, standard deviations, independent samples t-tests, one-way analysis of variance, exploratory factor analysis, multiple stepwise regression analysis and network analysis were used for data analysis. RESULTS: 'I don't look like myself' and itching were core and bridge symptoms, while bloating and lack of energy were sentinel symptoms in decompensated cirrhotic patients. Monthly family income, anxiety, depression, social support and disease duration influenced the neuropsychological symptom cluster, with worrying as the strongest predictor symptom. Influential factors for cirrhosis-specific symptom cluster included Child-Pugh class, monthly family income, disease duration, anxiety and depression, with itching being the strongest predictor symptom. Monthly family income, disease duration and depression were influential factors for gastrointestinal symptom cluster, with loss of appetite as the strongest predictor symptom. CONCLUSIONS: Neuropsychological, cirrhosis-specific and gastrointestinal symptom clusters were formed in decompensated cirrhotic patients. Through network analysis, direct connections between symptoms, symptom clusters and their influencing factors were revealed, thereby offering clinicians a foundation for effectively managing patients' pre-hospital symptoms. IMPACT: Decompensated cirrhosis patients commonly have multiple symptoms, while the management of pre-hospital symptoms is often suboptimal. This study identified neuropsychological, cirrhosis-specific, gastrointestinal symptom clusters and recognized core, bridge and sentinel symptoms in these patients. It also revealed the most prominent symptoms within each cluster. This provides insight into the hierarchy of symptoms, improving symptom management in decompensated cirrhosis. PATIENT AND PUBLIC INVOLVEMENT: There was no patient or public involvement.

Assessing Work Functioning in Patients with Persistent Low Back Pain: Exploring the Structural Validity of the Work Rehabilitation Questionnaire.

PURPOSE: Assessing work functioning in patients with persistent low back pain (LBP) is important for understanding their ability to engage in work-related activities. This study aims to evaluate the item characteristics, factor structure, and internal consistency of the Work Rehabilitation Questionnaire (WORQ) in patients with persistent LBP. METHODS: Four hundred and twenty-five individuals with LBP completed the WORQ. Item characteristics, exploratory factor analysis (EFA), and consistency were performed to identify the underlying factors. RESULTS: Missing responses were < 2% for each item. The analysis revealed three factors: psychological wellbeing, physical functioning, and cognitive ability. The factors demonstrated strong internal consistency, with Cronbach's alpha values ranging from 0.88 to 0.93 and McDonald's Omega from 0.92 to 0.96. Fifteen items did not fit into any identified factors, suggesting their potential value in screening functioning levels beyond the factors. CONCLUSIONS: The WORQ is a valid instrument for evaluating work limitations in individuals with persistent LBP. Further research should assess its responsiveness to changes from interventions that target workability. Advancing this knowledge has the potential to promote work rehabilitation and improve the quality of life for patients with persistent LBP.

Determining Sample Size Requirements in EFA Solutions: A Simple Empirical Proposal.

In unrestricted or exploratory factor analysis (EFA), there is a wide range of recommendations about the size samples should be to attain correct and stable solutions. In general, however, these recommendations are either rules of thumb or based on simulation results. As it is hard to establish the extent to which a particular data set suits the conditions used in a simulation study, the advice produced by simulation studies is not direct enough to be of practical use. Instead of trying to provide general and complex recommendations, in this article, we propose to estimate the sample size that is needed to analyze a data set at hand. The estimation takes into account the specified EFA model. The proposal is based on an intensive simulation process in which the sample correlation matrix is used as a basis for generating data sets from a pseudo-population in which the parent correlation holds exactly, and the criterion for determining the size required is a threshold that quantifies the closeness between the pseudo-population and the sample reproduced correlation matrices. The simulation results suggest that the proposal works well and that the determinants identified agree with those in the literature.

Stakeholders' perspectives on critical success factors for environmental social and governance (ESG) implementation.

Environmental Social and Governance (ESG) has emerged as a sensitive issue, attracting the attention of a large audience that could not be ignored. Government bodies continue to pass regulations mandating organizations to comply with ESG principles. However, many organizations have had unsatisfactory results while promoting sustainability ideals due to various challenges. To achieve ESG goals, the present study offers a thorough framework for ESG implementation across organizations based on the critical success factor (CSF) theory and the opinions of diverse stakeholders. Following a literature review and brainstorming with ESG experts and academicians, a survey questionnaire was sent to 400 respondents to evaluate the 20 factors identified as 'super-set' CSFs for ESG implementation. This paper represents a novel attempt in ESG research as it conducts a survey supported by exploratory factor analysis (EFA). The interactions between the significant CSFs were studied by employing total interpretative structural modeling (TISM) and fuzzy MICMAC approach. ESG implementation is found to be highly driven by firm characteristics, earnings quality, and environmental performance, which can be argued to be the fundamental determinants of ESG implementation. According to these findings, organizations' leadership should (1) focus on effectively structuring firms' attributes to swiftly operate the ESG implementation framework, (2) ensure consistent business earnings that signal improved future performance, and (3) continuously monitor and assess their environmental performance. These efforts should be supported by engaging with diverse stakeholder groups, each playing its respective role in ESG implementation. Consequently, ESG implementation across organizations is anticipated to accelerate with thoughtful organizational coordination, strategic planning, and compliance with authoritative policies. However, rather than solely focusing on creating ESG disclosure laws, policymakers should also focus on creating better ESG outcomes through effective ESG implementation. Therefore, this research offers valuable insights into improving ESG practices, which facilitates the adoption of mandatory ESG disclosure regulations.

Development and validation of a rating scale for barriers to and facilitators of nurses' participation intentions in "Internet + Nursing Service".

BACKGROUND: Given increases in China's aging population, the growing demand for public health services and the shortage of human resources among nurses have become more prominent. Under such a background, "Internet + Nursing Services" have received more attention. Thus, exploring the barriers to and facilitators of nurses' willingness to participate in "Internet + Nursing Services" and utilizing internet technology to increase the supply of nursing services has become a key issue. OBJECTIVE: This study aimed to develop a scale for assessing the barriers to and facilitators of nurses' willingness to participate in "Internet + Nursing Services" and to test the validity and reliability of the scale. METHODS: A preliminary scale was developed based on a literature review, theoretical research, semistructured qualitative interviews, and two rounds of Delphi expert inquiry. A convenient sampling method was used for the questionnaire survey. A 5-point Likert scale was used to evaluate the importance of the items. The survey data of 659 clinical nurses obtained from February to March 2023 were used for item analysis, exploratory factor analysis (EFA), and reliability and validity tests of the scale. The survey data of 538 clinical nurses obtained in April 2023 were used for confirmatory factor analysis (CFA) of the final scale. RESULTS: The final scale consists of 25 items and 4 dimensions (performance expectations, perceived risk, need for professional knowledge training, and nonprofessional knowledge training). The scale showed good structural validity and content validity: the Cronbach's α coefficient of the scale was 0.955, the split-half reliability was 0.778, the test-retest reliability was 0.944, the kaiser-meyer-olkin(KMO) value was 0.960, and the cumulative variance contribution rate of the 4 common factors was 83.147%. The scale content validity index(S-CVI) was 0.914. The confirmatory factor analysis model had favorable fit indices: χ2/df = 4.234, RMSEA = 0.078, NFI = 0.940, IFI = 0.953, TLI = 0.947, and CFI = 0.953. CONCLUSION: The scale for assessing the barriers to and facilitators of nurses' willingness to participate in "Internet + Nursing Services" has good reliability and validity, and provides a reference for evaluating nurses' willingness to participate in "Internet + Nursing Services".

Development of an infection control competency scale for clinical nurses: an instrument design study.

BACKGROUND: Nurses work in close proximity to patients, and as such, they can have a direct impact on the control of infections; thus, it is important for nurses to be competent in infection control. However, the scales used to measure infection control performance in nurses are not suitable for measuring infection control competencies that reflect nurses' expertise, clinical environment, and work. Thus, this study aimed to develop a valid and reliable measure to assess infection control competency of clinical nurses. METHODS: A concept analysis, using a hybrid model, was performed on the infection control competency of clinical nurses to confirm the components and develop 67 initial items. Ten experts evaluated the content validity of these items, and a Korean language expert and a Doctor of Nursing reviewed the questions to consolidate them into 59 items. Subsequently, 267 nurses working at a certified tertiary hospital in D City were surveyed to confirm the validity and reliability of the scale. RESULTS: As a result of the study, the final scale comprising seven factors and 33 questions was derived, and the cumulative explanatory power of these factors was 60.8%. To verify convergent and discriminant validity, confirmatory factor analysis was conducted, and the average variance extraction index, composite reliability values, and confidence interval of the correlation coefficient between factors were confirmed. Convergent and discriminant validities were verified by comparison with standard values. The Cronbach's α for the entire scale in this study was 0.93. Consequently, the validity and reliability of the clinical nurses' infection control competency measurement scale were verified. CONCLUSIONS: The validity and reliability of the infection control competency measurement scale for clinical nurses (ICCS-CN) developed in this study was verified, and the scale can be effectively used to measure the infection control competency of clinical nurses. Measuring the infection control competency of clinical nurses will help reduce the harm caused by infection and ensure patient safety by decreasing infection rates in medical institutions.

Development of the ethical decision-making competence scale.

BACKGROUND: Developing confident capacity for ethical decision-making is vital in nursing education. However, no tool examines nursing students' competence in ethical decision-making. AIM: This study aimed to develop an Ethical Decision-Making Competence Scale (EDM-CS) to assess ethical care decision-making competencies in nursing students. PARTICIPANTS AND RESEARCH CONTEXT: Original items were obtained by employing a focus group and the Delphi method. A cross-sectional design was used to confirm the items remained on the scale. Additionally, the scale's reliability and validity were assessed. The EDM-CS was completed by 498 nursing students. An exploratory factor analysis (EFA) was used to examine the factor structure based on data from group 1 (n = 250). A second-order confirmatory factor analysis (CFA) was used to examine the model's fitness based on data from group 2 (n = 248). This study was conducted between August 2022 and July 2023. ETHICAL CONSIDERATIONS: The Institutional Review Board of Chung Shan Medical University Hospital approved this study's design and procedure. RESULTS: From the original 34 items, nine were deleted in the EFA. Thus, the EDM-CS had 25 items and a four-factor structure (ethical judgement, ethical sensitivity, ethical motivation, and ethical action), which explained 60.97% of the total variance. A second-order CFA identified a second-order factor termed 'ethical decision-making competence' with 18 items (root mean square residual = 0.052). The EDM-CS scores correlated significantly and positively with the scores on the Scale of Protective Factor-24 (r = 0.47, p < .001), which indicated good convergent validity. Cronbach's alpha coefficient of the final EDM-CS was 0.90 and ranged from 0.73 to 0.80 for the four subscales. DISCUSSION AND CONCLUSION: The EDM-CS was validated to fit the data adequately. It can be used to evaluate clinical nursing students' ethical decision-making abilities and to develop education strategies to improve their ethical care competence.

The comparison data forest: A new comparison data approach to determine the number of factors in exploratory factor analysis.

Developing psychological assessment instruments often involves exploratory factor analyses, during which one must determine the number of factors to retain. Several factor-retention criteria have emerged that can infer this number from empirical data. Most recently, simulation-based procedures like the comparison data approach have shown the most accurate estimation of dimensionality. The factor forest, an approach combining extensive data simulation and machine learning modeling, showed even higher accuracy across various common data conditions. Because this approach is very computationally costly, we combine the factor forest and the comparison data approach to present the comparison data forest. In an evaluation study, we compared this new method with the common comparison data approach and identified optimal parameter settings for both methods given various data conditions. The new comparison data forest approach achieved slightly higher overall accuracy, though there were some important differences under certain data conditions. The CD approach tended to underfactor and the CDF tended to overfactor, and their results were also complementary in that for the 81.7% of instances when they identified the same number of factors, these results were correct 96.6% of the time.

Factor retention in ordered categorical variables: Benefits and costs of polychoric correlations in eigenvalue-based testing.

An essential step in exploratory factor analysis is to determine the optimal number of factors. The Next Eigenvalue Sufficiency Test (NEST; Achim, 2017) is a recent proposal to determine the number of factors based on significance tests of the statistical contributions of candidate factors indicated by eigenvalues of sample correlation matrices. Previous simulation studies have shown NEST to recover the optimal number of factors in simulated datasets with high accuracy. However, these studies have focused on continuous variables. The present work addresses the performance of NEST for ordinal data. It has been debated whether factor models - and thus also the optimal number of factors - for ordinal variables should be computed for Pearson correlation matrices, which are known to underestimate correlations for ordinal datasets, or for polychoric correlation matrices, which are known to be instable. The central research question is to what extent the problems associated with Pearson correlations and polychoric correlations deteriorate NEST for ordinal datasets. Implementations of NEST tailored to ordinal datasets by utilizing polychoric correlations are proposed. In a simulation, the proposed implementations were compared to the original implementation of NEST which computes Pearson correlations even for ordinal datasets. The simulation shows that substituting polychoric correlations for Pearson correlations improves the accuracy of NEST for binary variables and large sample sizes (N = 500). However, the simulation also shows that the original implementation using Pearson correlations was the most accurate implementation for Likert-type variables with four response categories when item difficulties were homogeneous.

Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance.

PURPOSE: This study aimed to examine the satisfaction of dental professionals, including dental students, dentists, and dental technicians, with computer-aided design (CAD) software performance using deep learning (DL) and explainable artificial intelligence (XAI)-based behavioral analysis concepts. MATERIALS AND METHODS: This study involved 436 dental professionals with diverse CAD experiences to assess their satisfaction with various dental CAD software programs. Through exploratory factor analysis, latent factors affecting user satisfaction were extracted from the observed variables. A multilayer perceptron artificial neural network (MLP-ANN) model was developed along with permutation feature importance analysis (PFIA) and the Shapley additive explanation (Shapley) method to gain XAI-based insights into individual factors' significance and contributions. RESULTS: The MLP-ANN model outperformed a standard logistic linear regression model, demonstrating high accuracy (95%), precision (84%), and recall rates (84%) in capturing complex psychological problems related to human attitudes. PFIA revealed that design adjustability was the most important factor impacting dental CAD software users' satisfaction. XAI analysis highlighted the positive impacts of features supporting the finish line and crown design, while the number of design steps and installation time had negative impacts. Notably, finish-line design-related features and the number of design steps emerged as the most significant factors. CONCLUSIONS: This study sheds light on the factors influencing dental professionals' decisions in using and selecting CAD software. This approach can serve as a proof-of-concept for applying DL-XAI-based behavioral analysis in dentistry and medicine, facilitating informed software selection and development.

Symptom burden survey and symptom clusters in patients with cervical cancer: a cross-sectional survey.

PURPOSE: The purpose of this study is to determine the incidence and severity of symptoms of patients with cervical cancer within 6 months after radiotherapy and chemotherapy, form a symptom burden report, evaluate the distribution characteristics of symptoms, identify symptom clusters, and provide a basis for clinical doctors and nurses to improve the symptom management of patients with cervical cancer after radiotherapy and chemotherapy. METHODS: The patients with cervical cancer within 6 months after radiotherapy and chemotherapy were recruited to investigate their symptom burden. Exploratory factor analysis was used to identify symptom clusters. RESULTS: A total of 250 patients participated in the study. The study found that the most common symptom among the 40 symptoms was fatigue, and the most serious symptom was nocturia. Based on the occurrence rate and severity of symptoms, nine symptom clusters were identified, including psycho-emotion-related symptom cluster, pain-disturbed sleep-related symptom cluster, menopausal symptom cluster, tinnitus-dizziness-related symptom cluster, urinary-related symptom cluster, dry mouth-bitter taste-related symptom cluster, intestinal-related symptom cluster, memory loss-numbness-related symptom cluster, and emaciation-related symptom cluster. The three most serious symptom clusters are pain-disturbed sleep-related symptom cluster, urinary-related symptom cluster, and memory loss-numbness-related symptom cluster. CONCLUSION: The symptoms of patients with cervical cancer within 6 months after radiotherapy and chemotherapy are complex, and nine symptom clusters can be identified according to the incidence and severity of symptoms. We can find the potential biological mechanism of each symptom cluster through the discussion of previous mechanism research and clinical research. The number of symptom clusters and the number of symptoms within the symptom cluster are closely related to the symptom evaluation scale selected for the study. Therefore, the symptom cluster study urgently needs a targeted symptom evaluation scale that can comprehensively reflect the patient's condition.