Best-fit Model Research Articles

Validity of the E-Cigarette Wisconsin Inventory of Smoking Dependence Motives (e-WISDM) in Exclusive E-Cigarette Users: Evidence from a Laboratory Self-Administration Study.

Prior research suggests that the e-Cigarette Wisconsin Inventory of Smoking Dependence Motives (e-WISDM) distinguishes primary (e-PDM) and secondary dependence (e-SDM), however, there is little research on these e-WISDM dimensions and prior research comprised dual users (using cigarettes and e-cigarettes) and those using older generations of e-cigarettes. Those exclusively using contemporary e-cigarettes (N = 164) completed the e-WISDM and a laboratory self-administration session and rated pre-use expectancies and post-use experiences. Only a 1-factor model limited to the primary scales (Automaticity, Tolerance, Craving, Loss of Control) achieved good model fit. The e-PDM was correlated with the Penn State Electronic Cigarette Dependence Index (PS-ECDI), r = .79, p < .001. The e-PDM and PS-ECDI were similarly correlated with use topography and self-reported measures. Analyses of motive profiles identified Taste, Tolerance, and Automaticity as the most strongly endorsed motives in the full sample. Subgroup analyses indicated primary motives were more elevated in in daily vs. non-daily e-cigarette users and participants with vs. without a history of smoking cigarettes. Taste motives were stronger in users of 3rd vs. 4th generation e-cigarettes. These findings suggest that the four e-PDM subscales are a concise, reliable, and valid measure of core e-cigarette dependence motives that are related to meaningful dependence attributes. Electronic cigarettes (e-cigarettes) are dependence-producing. Instruments that measure e-cigarette dependence are necessary to identify users who may have difficulty quitting e-cigarettes and who are at risk for use-related harms. The four subscales of the e-WISDM PDM index self-reported heavy e-cigarette use, craving, automatic or mindless use, and perceived loss of control over use. The current research supports the validity of the e-WISDM PDM as a measure of core e-cigarette dependence in users of today's e-cigarette devices.

A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation

BackgroundThis study aimed to develop a nomogram for predicting persistent renal dysfunction in acute kidney injury (AKI) following lung transplantation (LTx).MethodA total of 229 LTx patients were enrolled, and genotyping for 153 single nucleotide polymorphisms (SNPs) was performed. The cohort was randomly divided into training (n = 183) and validation (n = 46) sets in an 8:2 ratio. Statistically significant SNPs identified through pharmacogenomic analysis were combined with clinical factors to construct a comprehensive prediction model for persistent AKI using multivariate logistic regression analysis. Discrimination and calibration analyses were conducted to evaluate the performance of the model. Decision curve analysis was used to assess its clinical utility. Due to the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model.ResultsThe final nomogram comprised nine predictors, including body mass index, thrombin time, tacrolimus initial concentration, rs757210, rs1799884, rs6887695, rs1494558, rs2069762 and rs2275913. In the training set, the area under the receiver operating characteristic curve of the nomogram was 0.781 (95%CI: 0.715–0.846), while in the validation set it was 0.698 (95%CI: 0.542–0.855), indicating good model fit. As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Additionally, decision curve analysis confirmed its clinical applicability.ConclusionThis study presents a genotype-guided nomogram that can be used to assess the risk of persistent AKI following LTx and may assist in guiding personalized prevention strategies in clinical practice.

Impact of eptinezumab on work productivity beyond reductions in monthly migraine days: post hoc analysis of the DELIVER trial

BackgroundEptinezumab’s impact on self-reported work productivity in adults with migraine and 2‒4 prior preventive migraine treatment failures is not fully understood.MethodologyElectronic diaries captured monthly migraine days (MMDs) reported by patients enrolled in the randomized, double-blind, placebo-controlled DELIVER trial (NCT04418765). The migraine-specific Work Productivity and Activity Impairment questionnaire, administered at baseline and each monthly visit, was a secondary outcome of DELIVER and used to model changes from baseline in self-reported monthly hours of absenteeism (decreased work attendance) and presenteeism (reduced work efficiency while at work with a migraine) in Canada, as the base case. Path analysis illustrated eptinezumab’s impact on work productivity beyond MMDs.ResultsAs MMDs increased, monthly hours of absenteeism increased linearly while those of presenteeism increased quadratically. Best-fit models were improved after including an eptinezumab treatment effect, showing benefit over placebo after controlling for MMD frequency. Compared to placebo, patients treated with eptinezumab (pooled) had a modeled reduction from baseline of 7.2 h/month (absenteeism) (95% CI: −9.9, −4.5; P < 0.001) and 21.4 h/month (presenteeism) (95% CI: −26.3, −16.5; P < 0.001) over weeks 1‒24. Beyond MMD reductions, improvements in patient-identified most bothersome symptom (PI-MBS) and reductions in percent of severe migraine attacks contributed to eptinezumab’s effect.ConclusionsEptinezumab decreased absenteeism and presenteeism based on patient reports, with data highlighting the importance of considering the PI-MBS. The greater change from baseline than placebo in self-reported absenteeism and presenteeism was only partly explained by changes in MMDs, presenting a potential opportunity to decrease the cost of migraine in the workplace.Trial registrationClinicalTrials.gov (Identifier: NCT04418765); EudraCT (Identifier: 2019-004497-25)

Psychometric properties of the Wijma Delivery Expectancy/Experience Questionnaire version A among Chinese expectant fathers

BackgroundEvidence on the mental status of expectant fathers indicates that their fears of childbirth (FOC) are of great concern. In consideration of expectant fathers’ increasing involvement during pregnancy and childbirth, it is necessary to provide a reliable instrument to measure the psychometric properties of expectant fathers. The purpose of this study was to revise the Wijma Delivery Expectancy/Experience Questionnaire version A (W-DEQ A) for expectant fathers, analyze the psychometric properties and explore its applicability to the Chinese population.MethodsThe W-DEQ A was revised to apply to Chinese men following the completion of a cross-sectional study. Content validity was assessed by an expert panel of specialists in obstetrics. Construct validity was assessed using exploratory factor analysis and confirmatory factor analysis. Reliability was assessed by the internal consistency coefficient and retest reliability.ResultsA total of 741 expectant fathers participated in this study. The item-level content validity index (I-CVI) was greater than 0.8, while the scale-level CVI (S-CVI) was 0.92, indicating satisfactory content validity. Exploratory factor analysis revealed eight factors, accounting for 60.97% of the cumulative variance. Confirmatory factor analysis revealed acceptable structural validity of the father’s version of the W-DEQ A (W-DEQ AF), demonstrating a good model fit. The Cronbach’s alpha for the W-DEQ AF was 0.843, indicating good reliability of the scale.ConclusionsThe Chinese version of the W-DEQ-AF is reliable and could be considered for use with expectant fathers in China to assess their psychological status and fear of childbirth during pregnancy of their spouse.

The ALMA-CRISTAL Survey. Spatial extent of [CII] line emission in star-forming galaxies at z=4-6

We investigate the spatial extent and structure of the line emission in a sample of 34 galaxies at $z=4-6$ from the Resolved ISM in STar-forming galaxies with ALMA (CRISTAL) Survey. By modeling the distribution of the line emission in the interferometric visibility data directly, we derive the effective radius of line emission assuming an exponential profile. These measurements comprise not only isolated galaxies but also interacting systems that were identified thanks to the high spatial resolution of the data. The line radius ranges from 0.5 to 3.5 kpc with an average value of $ e CII kpc. We compare the sizes with the sizes of rest-frame ultraviolet (UV) and far-infrared (FIR) continua, which were measured from the HST F160W images and ALMA Band-7 continuum images, respectively. We confirm that the line emission is more spatially extended than the continuum emission, with average size ratios of $ e CII /R_ e UV and $ e CII /R_ e FIR although about half of the FIR-detected sample shows a comparable spatial extent between the line and the FIR continuum emission e CII e FIR $). The residual visibility data of the best-fit model do not show statistical evidence of flux excess, indicating that the line emission in star-forming galaxies can be characterized by an extended exponential disk profile. Overall, our results suggest that the spatial extent of line emission can primarily be explained by photodissociation regions associated with star formation activity, while the contribution from diffuse neutral medium (atomic gas) and the effects of past merger events may further expand the line distributions, causing their variations among our sample. Finally, we report the negative correlation between the surface density ($ CII $) and the Lyalpha equivalent width (EW$_ Ly alpha $), and a possible negative correlation between $R_ e CII /R_ e UV $ and $ EW Ly alpha $, which may be in line with the scenario that atomic gas component largely contributes to the extended line emission. Future three-dimensional analysis of Lyalpha and Halpha lines will shed light on the association of the extended line emission with atomic gas and outflows.

Factors associated with time to first birth interval among ever married Bangladeshi women: A comparative analysis on Cox-PH model and parametric models.

The fertility rate of a married woman can be measured by the length of the first birth interval (FBI). This length is influenced by some significant factors. Better knowledge about the factors affecting the birth interval can help in controlling population growth and fertility progress. The main focus of this study was to compare the performance of Cox-Proportional Hazard (Cox-PH) and the parametric Accelerated Failure Time (AFT) model in assessing the impact of significant factors affecting the time to FBI of ever-married Bangladeshi women. Information of 14941 women having at least one birth was included in this study from the most recent nationally representative data 2017-18 Bangladesh Demographic and Health Survey (BDHS). We used the Cox-PH model and AFT model under various parametric forms of survival time distributions (Weibull, Exponential, and Log-normal distribution) to measure the effect of factors influencing FBI. And then, a respective Akaike information criterion (AIC) was calculated for selecting the best-fitted model. According to the AIC and BIC values, the log-normal model fitted better than other AFT models. Based on the log-normal model, women's age and age at first marriage, maternal and paternal education, contraceptive use status, used anything to avoid pregnancy, sex of household head, and spousal age difference had a significant association with FBI of ever married Bangladeshi women. The parametric AFT model (log-normal distribution) was a better fitted model in evaluating the covariates associated with FBI of ever-married Bangladeshi Women. Higher education, the right age at marriage, and proper knowledge about family planning (i.e., contraception use) should be ensured for every married person to control the gap of the first birth.

Uncovering the invisible: A study of Gaia18ajz, a candidate black hole revealed by microlensing

Identifying black holes is essential for our understanding of the development of stars and can reveal novel principles of physics. Gravitational microlensing provides an exceptional opportunity to examine an undetectable population of black holes in the Milky Way. In particular, long-lasting events are likely to be associated with massive lenses, including black holes. We present an analysis of the Gaia18ajz microlensing event reported by the Gaia Science Alerts system. Gaia18ajz is a long-timescale event exhibiting features indicative of the annual microlensing parallax effect. Our objective is to estimate its lens parameters based on the best-fitting model. We used photometric data obtained from the Gaia satellite and terrestrial observatories to investigate a variety of microlensing models and calculate the most probable mass and distance to the lens, taking into consideration a Galactic model as a prior. Subsequently, we applied a mass--brightness relation to evaluate the likelihood that the lens is a main sequence star. We also describe the (DLC), an open-source routine that computes the distribution of probable lens mass, distance, and luminosity employing the Galaxy priors on stellar density and velocity for microlensing events with detected microlensing parallax. We modelled the Gaia18ajz event and found its two possible models, the most probable Einstein timescales for which are $316^ $ days and $299^ $ days. Applying Galaxy priors for stellar density and motion, we calculated a most probable lens mass of $4.9^ M_ located at kpc $ and a less probably mass of $11.1^ M_ located at kpc $. Our analysis of the blended light suggests that the lens is likely a dark remnant of stellar evolution rather than a main sequence star.

Impact of calcination temperature, organic additive percentages, and testing temperature on the rheological behaviour of dried sewage sludge

Abstract The main objective of the present work is to evaluate the influence of calcination pretreatment (600–1,000°C), organic additive incorporation (4% methocel, 4% amijel, and 8% starch), and testing temperature (20–60°C) on the rheological flow behaviour of dried sewage sludge and sewage sludge ashes. Besides, the dependency of sludge systems rheology on total solid content (4–15%) and methocel percentage (3–6%) was also evaluated. Furthermore, characterization techniques such as thermal gravimetric analysis-differential scanning calorimetry, X-ray fluorescence, X-ray diffraction, Brunauer–Emmett–Teller, and scanning electron microscopy were employed to investigate, respectively, the thermal decomposition, the chemical composition, the structural variations, the specific surface area, the surface morphology, and microstructure of sludges. The analysis of rheological characteristics according to best-fitting rheological models such as Herschel–Bulkley, Ostwald–de Waele, Cross, and Carreau models revealed that the yield stress (τ 0) and infinite apparent viscosity (η ∞) increase with an increase in TS or methocel percentage and decrease with increasing calcination or testing temperature. The strong impact of testing temperature concerning the reduction of the viscosity involves high activation energy (E a). This last criterion was used to compare the inter-particle strength of sludge systems.

Psychometric Evaluation of the Persian Version of the Tinnitus Cognitions Questionnaire

Background and Aim: Cognitive Behavioral Therapy (CBT) is the only evidence-based and method for tinnitus management. To assess a patient’s progress during and after CBT, an assessment tool such as a questionnaire is needed. Since there is no validated tool available in Iran for this purpose, this study aims to investigate the psychometric properties of the Persian version of the Tinnitus Cognition Questionnaire (P-TCQ). Methods: The TCQ was first translated to Persian based on the international quality of life assessment protocol. participants were 102 tinnitus patients and 7 audiologists. After translation and data collection, psychometric properties (content validity, face validity, construct validity, criterion validity, reliability) of the P-TCQ were evaluated. Results: The Content Validity Ratio (CVR) and Item-Content Validity Index (I-CVI) values were higher than the minimum acceptable values (0.74 and 0.79, respectively). Item-level Face Validity Index (I-FVI) and average of Scale-level Face Validity Indexes (S-FVI) were in a range of 0.8-0.96 and 0.88–0.90, respectively, all of which were higher than the acceptable value of 0.7. Furthermore, fit indices had higher than the acceptable values and showed the good fit of the confirmatory factor abalysis model. There was a positive correlations between the TCQ total scores and the tinnitus handicap inventory (r=0.60) and hospital anxiety and depression scale (r=0.61), indicating acceptaiuoble criterion validity. Furthermore, Cronbach’s alpha coefficient and the intraclass correlation coefficient showed that the TCQ has acceptable reliability. Conclusion: The P-TCQ is a valid and reliable questionnaire to evaluate the cognitive aspects of tinnitus in the Iranian samples.Keywords: Tinnitus; cognition; tinnitus cognitions questionnaire; cognitive behavioral therapy; tinnitus handicap inventory

A Cognitive Diagnostic Model of Reading Ability based on China’s Standards of English Language Ability

Traditional English reading assessments in China typically provide a single summative score, without revealing the extent to which students have mastered different aspects of reading comprehension. Cognitive Diagnostic Models (CDMs), as a type of multidimensional latent trait model, offer diagnostic insights by presenting examinees' proficiency levels across a set of predefined skills. Cognitive diagnostic assessments based on these models can deliver multidimensional, fine-grained evaluation results, thus overcoming the limitations of traditional unidimensional score-based assessments. Based on the China's Standards of English Language Ability (CSE), this study constructs a cognitive diagnostic assessment model for English reading. Using college general English reading test papers and the G-DINA model, the English reading abilities of 200 students were diagnosed, and personalized performance reports were generated. A questionnaire survey was conducted to gather students' evaluations and suggestions regarding the feedback reports. The results indicate that the cognitive diagnostic model for English reading, constructed according to the CSE, exhibits good model fit and high diagnostic reliability. The personalized performance reports visually and comprehensively present the strengths and weaknesses of the test takers' reading abilities, providing valuable guidance for students' future remedial learning. This study utilizes a multidimensional assessment approach based on cognitive diagnostic models to evaluate the English reading abilities of Chinese university students, effectively addressing the limitations of traditional assessment methods that typically offer only a single score.

Using spatial analysis and variable selection of traffic violations to determine crash frequency

Numerous studies have determined a relationship between traffic violations and crash risk. However, not only have the results been inconsistent, but the effects of minor traffic violations have not been studied extensively. In addition, the characteristics of violation data (spatial autocorrelation) are widely ignored. Hence, various types of traffic violations and spatial variable selection methods (stepwise regression, adaptive LASSO, random forest, and Boruta) were employed to select key violation variables that could be used to predict crash frequency. Then, the study used Geographically Weighted Negative Binomial Regression (GWNBR) model to determine their varying associations with crash frequency. The results indicated that Boruta had the lowest error in variable selection, and GWNBR was the best-fitting model with a heterogeneous association between crash frequency and traffic violations. The study found that areas with a higher percentage of specific behaviour-related violations and other minor violations (illegal parking) were related to higher crash frequency.

Validation of the 21st Century Skills Assessment Scale for public health students in Thailand: a methodological study.

This study aimed to develop and validate the 21st Century Skills Assessment Scale (21CSAS) for Thai public health (PH) undergraduate students using the Partnership for 21st Century Skills framework. A cross-sectional survey was conducted among 727 first- to fourth-year PH undergraduate students from 4 autonomous universities in Thailand. Data were collected using self-administered questionnaires between January and March 2023. Exploratory factor analysis (EFA) was used to explore the underlying dimensions of 21CSAS, while confirmatory factor analysis (CFA) was conducted to test the hypothesized factor structure using Mplus software (Muthén & Muthén). Reliability and item discrimination were assessed using Cronbach's α and the corrected item-total correlation, respectively. EFA performed on a dataset of 300 students revealed a 20-item scale with a 6-factor structure: (1) creativity and innovation; (2) critical thinking and problem-solving; (3) information, media, and technology; (4) communication and collaboration; (5) initiative and self-direction; and (6) social and cross-cultural skills. The rotated eigenvalues ranged from 2.12 to 1.73. CFA performed on another dataset of 427 students confirmed a good model fit (χ2/degrees of freedom=2.67, comparative fit index=0.93, Tucker-Lewis index=0.91, root mean square error of approximation=0.06, standardized root mean square residual=0.06), explaining 34%-71% of variance in the items. Item loadings ranged from 0.58 to 0.84. The 21CSAS had a Cronbach's α of 0.92. The 21CSAS proved be a valid and reliable tool for assessing 21st century skills among Thai PH undergraduate students. These findings provide insights for educational system to inform policy, practice, and research regarding 21st-century skills among undergraduate students.

Prediction of cyanotic and acyanotic congenital heart disease using machine learning models.

Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality. To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors. The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan, Pakistan from December 2017 to October 2019. A sample of 3900 mothers whose children were diagnosed with cyanotic or acyanotic congenital heart disease was taken. Multivariate outlier detection methods were used to identify the potential outliers. Different machine learning models were compared, and the best-fitted model was selected using the area under the curve, sensitivity, and specificity of the models. Out of 3900 patients included, about 69.5% had acyanotic and 30.5% had cyanotic congenital heart disease. Males had more cases of acyanotic (53.6%) and cyanotic (54.5%) congenital heart disease as compared to females. The odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during pregnancy. The artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012, sensitivity of 65.76%, and specificity of 97.23%. Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease. Males are more at risk and their mothers need more care, good food, and physical activity during pregnancy. The best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural network. The results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan.

Effects of Peatland Fires on Above-ground Carbon Stocks in Kepulauan Meranti Regency, Riau Province

Peat fires substantially alter ecosystem dynamics and carbon storage, making it essential to understand how fire-related components affect post-fire carbon stocks. This study aims to estimate the above-ground carbon stock on burned peatlands in Kepulauan Meranti Regency, Riau Province, and examine how fire recurrence, last fire occurrence, and burn severity influence the carbon stock using a modified regression model and remote sensing data. The normalized burn ratio index difference between post- and pre-fire was used to calculate burn severity. The continuous predictor variable was transformed using a natural logarithm to generate the best-fit model. The 2014 burned peatland stored the highest carbon, whereas the 2020 burned peatland was the lowest. The 2020 fire period was the most severe compared to the 2014 and 2018–2019 fires, although it had a smaller burned area. This study highlights that fire-related components significantly affect post-fire peatland above-ground carbon stocks, particularly last fire occurrence and burn severity. Meanwhile, fire recurrence had the weakest impact and correlation with above-ground carbon stock compared to other predictors, likely due to the brief intervals between fire events in 2018 and 2019, which may have restricted ecosystem recovery and limited carbon storage capacity.

Geospatial mapping and entropy-based analysis for groundwater evaluation with estimation of potential health risks due to nitrate and fluoride exposure.

Groundwater is a vital source of freshwater, but its quality is often compromised by various physiochemical factors. In the Mid-Gangetic Plains, there is a concerning escalation in the degradation of groundwater quality due to anthropogenic interventions. However, there remains a paucity of comprehensive knowledge concerning groundwater quality and the associated health hazards it poses. In response to this gap, the current study focuses on Nalanda district (Bihar), where 78 groundwater samples were collected across district in the month of May 2022 and their various water quality parameters were quantified as per standard methods. The adequacy of groundwater for human use was assessed using an entropy-based water quality index (EWQI), which also evaluated the potential human health risk stemming from nitrate and fluoride contamination. Furthermore, an empirical Bayesian kriging (EBK) driven geostatistical approach was utilized to predict water quality parameters at ungauged sites. The analysis of results disclosed that the ionic dominance in groundwater followed the sequence as cations Ca2+ > Mg2+, and anions HCO3- > SO42- > Cl- > NO3- > F- > PO43-. The concentration of NO3- and F- exceeded the permissible BIS levels by 11.5% and 6.5% of the samples respectively. The analysis of EBK models suggested K-Bessel as the best-fit model for pH, Mg2+, TH, F-, NO3-, and SO42- spatial interpolation while exponential EBK model for EC, Cl-, and PO43- and whittle EBK model for TDS, Ca2+, and HCO3- spatial interpolation. Spearman's correlation analysis revealed that elevated TDS and EC levels, coupled with correlations between NO3-, SO42-, and Cl-, suggest anthropogenic influences. The EWQI of the groundwater samples ranged from 36.28 to 180.80. The analysis of EWQI values revealed predominantly fair to good groundwater quality across the study area, suitable for drinking purposes. The hazard quotients for NO3- and F- indicate that non-carcinogenic health risks are more significant with nitrate pollution. The combined health impact was assessed using total hazard index (HI), ranging from 0.20 to 3.29 for children, 0.19 to 3.05 for males, and 0.17 to 2.70 for females. The cumulative probability distribution revealed total hazard index (HI) > 1 in 41.56%, 34.62%, and 28.21% of samples for children, males, and females. The HI analysis indicated a substantially higher risk for children compared to adults within the study area. This study offers a novel combination of entropy-based water quality assessment and geostatistical EBK modeling to evaluate groundwater quality and health risks in ungauged areas. The findings provide valuable insights for improved groundwater management and health risk mitigation.

Fostering Sustainable Workplace Through Leaders’ Compassionate Behaviors: Understanding the Role of Employee Well-Being and Work Engagement

Social sustainability in organizations refers to fostering well-being, inclusion, and the development of supportive relationships that enhance individual well-being and collective resilience. This study explores the relationship between compassionate leadership and work engagement, focusing on the mediating role of employee well-being. Compassionate leaders actively engage with their followers’ needs, offering emotional support and practical help and aligning with social sustainability principles by promoting positive workplace outcomes and fostering a supportive environment. Despite compassionate leadership having generated growing interest, very few studies have adopted a quantitative approach to examining its specific effects on employee outcomes, creating a significant gap in the literature specifically concerning how compassionate leadership affects employee engagement or additional variables, such as employee well-being. Framed in the Job Demands-Resources (JD-R) Model, our study addresses this gap by examining whether compassionate leadership can serve as a job resource by increasing work engagement through employee well-being as a personal resource. The study was carried out on a sample of 225 Spanish workers, and the data were examined through a structural equation model (SEM) to test the proposed model. The results showed a good model fit to the data, showing that well-being acts as a key mediator in the relationship between compassionate leadership and job engagement. Although we did not find a direct association between compassionate leadership and engagement, we found that compassionate behaviors significantly improve employee well-being, which in turn promotes higher engagement. These results extend the JD-R model by demonstrating that leaders’ compassionate leadership can serve as a job resource, contributing to a supportive and socially sustainable workplace.

Comparison of non-linear mixed effect models of the growth curve of commercial turkeys

The description of the growth curve in domestic animals is of importance in management and economic decision-making. The aim here was to determine the best non-linear mixed model to adjust the growth curve in commercial turkeys. The data come from an intensive turkey farm under a subhumid tropical climate. The live weight records of 266 female and 275 male turkeys, weighed weekly, from birth to 23 weeks, were used. The models of Gompertz, yt = A × exp(-b × exp(-k × t)), and von Bertalanffy, yt = A × (1-b × exp(-k × t))3 were used to estimate parameters and predict the growth curve; where: yt = live weight at the t-th week of age; A = the expected mature weight; b = the integration constant; k = the maturation rate. Six non-linear models using the Gompertz, and von Bertalanffy functions: one with only fixed effects, four mixed models considering the fixed, 1 to 3 random effects, and a last model including the random effect of turkey were used. The analyses were performed using the NLMIXED procedure of SAS, and the selection of the best-fit model was chosen based on the Akaike (AIC) and Bayesian (BIC) information criteria. AIC and BIC values improved with the inclusion of 1 to 3 random effects, in both models for females and males. Based on AIC and BIC criteria, the best mixed NLM was the model that included random effects for A, b, and k. However, the predicted weight values of the mixed models were similar.

Further analysis of determinants of Pentavalent and Measles immunizations dropouts among children under five years of age in Ethiopia from Mini-EDHS 2019

BackgroundGlobally, immunization prevents an estimated 2–3 million deaths from illnesses that can be prevented by vaccination. The predictors of Pentavalent and Measles immunization dropout were not investigated sufficiently. Therefore, this analysis was meant to determine the coverage, burden, and predictors of Pentavalent and Measles immunizations in Ethiopia.Materials and methodsThis study was a further analysis of the Mini Ethiopian Demographic Health Survey (EDHS) 2019, conducted with a nationally representative sample. In this study, immunization data was collected from a total sample of 3208 children of which 2004 (34.8%) children who received pentavalent 1 were considered for this study. Univariable, bi-variable, and multilevel mixed effect analysis was done using STATA version 17 Software. The mixed-effect model with the lowest AIC and BIC (information criteria) was chosen. A factor was designated as a significant predictor of immunization dropout if its p-value was less than 0.05 at 95% CI. The best-fitting model among the fitted models was ultimately chosen using the Akaike and Bayesian Information Criteria (AIC and BIC).ResultThe mean age of the head of the household was 37.6. The majority (68.9%) of the residents were from rural areas and of the total population, 64.9% have no formal education. Being residents of Afar increased the odds of dropping out by 3.28 (AOR = 3.28; 95% CI: 1.12, 9.56), whereas being residents of Addis Ababa reduced their odds of dropping out from prentavalent 3 by 68.1% (AOR = 0.319; 95% CI: 0.122, 0.833) compared to their Tigray counterparts. The dropout rate of Pentavalent 1 to measles 1 vaccination was 4.33 times higher among residents of Afar (AOR = 4.33; 95% CI: 1.38, 13.56). As the level of wealth increases, the quintile increases from poorer to richer compared to those in the poorest wealth quantile category, and marital status also affects the immunization dropout rate of their children.ConclusionsImmunization coverage of Ethiopian children was low and the dropout from pentavalent 1 and measles vaccination was high compared to the national and international targets to improve immunization coverage and reduce dropout. So different cost-effective interventions like Education provision for the community, decision-making facilitation, behavioral change support, and multi-system participation were required to increase vaccination coverage and reduce the vaccine dropout among children in Ethiopia.

Validation of the Modified Japanese 7C Readiness Scale for Parental Intentions of Child COVID-19 Vaccination

Parental willingness is a prerequisite for childhood immunization. This study evaluated whether parental vaccination readiness and willingness, measured using the modified Japanese 7C Vaccination Readiness Scale, predicted vaccination behavior. In December 2021, 2 months before the approval of COVID-19 vaccines for children aged 5–11 years in Japan, 1190 parents of children in this age group were surveyed regarding their intention to vaccinate their children against COVID-19. The survey utilized an online version of the modified 7C Vaccination Readiness Scale, comprising seven components: Confidence, Complacency, Constraints, Calculation, Collective Responsibility, Compliance, and Conspiracy. The modified 7C components were analyzed for model fit and their association with parental intention using structural equation modeling. The scale demonstrated good model fit (goodness-of-fit index: 0.98), with high standardized factor loadings for parental readiness (factor loadings: 0.84). In January 2024, the same parents were surveyed about their children’s COVID-19 vaccination status (valid responses: 536). The relationship between the components of the modified 7C scale and actual vaccination behavior was examined. Parents with low readiness to vaccinate their children against COVID-19 exhibited a significantly higher non-vaccination rate. These findings suggest that the modified 7C scale could support the development of targeted interventions to improve childhood vaccination uptake.

Longitudinal association between social participation and trajectories of life satisfaction in late middle-aged Korean adults with physical disabilities

Late middle-aged adults (50–64 years of age) who have physical disabilities often experience a decline in life satisfaction due to those bodily limitations. It is crucial to understand how their life satisfaction can be enhanced to support the social participation, health and wellbeing of this age group. This study examined the association between social participation and life satisfaction over time in Korean adults 50–64 years of age with physical disabilities. Data on 545 adults with physical disabilities aged 50–64 years from the 2016–2021 Korea Panel Survey of Employment for the Disabled (PSED) were extracted. The dependent variable was self-reported life satisfaction while social participation was the time-varying independent variable which was measured with a single question. The latent growth model (LGM) was used to examine the association between social participation and life satisfaction at each time point. The sample was composed of 313 (57.43 per cent) males and 232 (42.57 per cent) females from Korea having an average age of 57.42 years. A piecewise LGM demonstrated good model fit. Social participation was positively associated with life satisfaction at each time point for over the course of six years, with standardised coefficients ranging from 0.154 to 0.275 (p&lt;0.001). Social participation contributed significantly to life satisfaction in Korean adults 50–64 years of age with physical disabilities. The findings indicate the need for social participation that could improve the life satisfaction of those with physical disabilities.