Sample Size Selection Research Articles

Role of CSR in Promoting Happiness, Reducing Workplace Stress and Burnout in Indian Companies: A Study of Selected Indian Companies

This research paper analyzes the role of Corporate Social Responsibility (CSR) in promoting happiness and reducing workplace stress and burnout in Indian companies. The rationale behind selecting this topic is that human resources plays a significant role in every business, and effective management helps enhance the chances of the firm's success. However, in the contemporary highly competitive market, the demand from human resources is increasing, which is further creating the issue of long working hours, work-life imbalance, burden on meeting targets, etc., which creates the issue of stress, burnout, anxiety, depression, etc. which not only adversely impact their performance but also creates the issue of employee turnover and absenteeism. Hence, it has become essential for contemporary businesses to be concerned about taking CSR initiatives to enhance their sense of purpose, sense of belongingness and job satisfaction. For this research, Indian companies are selected for study purpose and their measures to reduce workplace stress and burnout are critically discussed. A secondary data collection method is used to conduct this research. Under this, Case Study Methodology is selected, which has supported the study of the measures taken by various Indian companies, namely Tata Consultancy Services (TCS), Mahindra& Mahindra, Reliance Industries Limited (RIL) and Infosys in promoting happiness to reduce workplace stress and burnout while improving their organizational productivity and performance. The study examines the positive impacts of CSR on employee well-being, particularly in promoting happiness, reducing stress, and preventing burnout. The research design's comprehensive approach and selected sample size enhance the study's reliability, clearly assessing CSR’s role in workplace wellness. The key findings of this study have supported the analysis that promoting happiness within the organization helps create a positive workplace environment, which further supports encouraging employees to showcase a positive attitude towards their job responsibility, team members and organizational success.

Performance analysis of vertical smart isolator based on magnetorheological elastomer

ABSTRACT As one of the field-dependent smart materials whose stiffness and damping properties can change instantaneously and reversibly, magnetorheological elastomer (MRE) has been extensively utilized on base isolation for vibration reduction. However, most previous studies have focused on the shear dynamic performance of MRE isolators, and there are few reports on the vertical extrusion performance of basic isolators within construction equipment. In this paper, MRE materials with strong bearing capacity and suitable vertical stiffness were fabricated by comprehensively considering the selection of raw materials, particle ratio, and sample size. The tension-compression viscoelasticity of the prepared MRE under varying magnetic fields and strain amplitudes was experimentally evaluated using an MRE axial MR testing system. Eventually, a single-degree-of-freedom dynamic test rig for vibration isolation of equipment in buildings, which integrates a vertical isolator with MRE as the core component, was constructed to perform a series of experimental investigations on the alterable frequency vibration capability of the designed MRE vertical isolator. The test results reveal that the controllable stiffness-damping properties of the prepared MRE in the axial tension-compression mode can enable the developed vertical isolator to exhibit superior frequency shifting and vibration attenuating characteristics in structural vibration.

Strategic Planning and Service Delivery at Kenya Power Headquarters in Kenya

Purpose: The major objective was to establish how strategic planning affected service delivery in KenyaPower. The study was based on the theory of contingency. Methodology: The study employed descriptive design. The target population consisted of 247 managers at various levels (low-level, mid-level and senior-level) from 11 departments at headquarters of Kenya Power. This study employed stratified random sampling techniques to select sample size of 114 staff. The study used primary data collection utilizing both open and closed-ended questionnaire. The questionnaires were distributed and collected using a drop-off and pick-up later method. The quantitative analysis used descriptive techniques such as frequency, percentage, standard deviations and means. The research also employed linear regression to examine the effect of strategic planning on service delivery. Findings: The research established that strategic planning (β=0.787; p=0.000) had substantial effects on service delivery at KenyaPower. Research concluded that strategic plannings have significant effects on delivery of services in Kenya Power. Unique Contribution to Theory, Policy and Practice: The study recommended that Kenya Power's executives should implement training programs for its workers. This would provide employees with the essential skills and abilities needed to improve the quality of service delivered to clients. Kenya Power should also make sure that its goals and objectives are clear and understandable to employees at every level by use of efficient communications.

Model-implied simulation-based power estimation for correctly specified and distributionally misspecified models: Applications to nonlinear and linear structural equation models.

Closed-form (asymptotic) analytical power estimation is only available for limited classes of models, requiring correct model specification for most applications. Simulation-based power estimation can be applied in almost all scenarios where data following the model can be estimated. However, a general framework for calculating the required sample sizes for given power rates is still lacking. We propose a new model-implied simulation-based power estimation (MSPE) method for the z-test that makes use of the asymptotic normality property of estimates of a wide class of estimators, the M-estimators, and give theoretical justification for the approach. M-estimators include maximum-likelihood, least squares estimates and limited information estimators, but also estimators used for misspecified models, hence, the new simulation-based power modeling method is widely applicable. The MSPE employs a parametric model to describe the relationship between power and sample size, which can then be used to determine the required sample size for a specified power rate. We highlight its performance in linear and nonlinear structural equation models (SEM) for correctly specified models and models under distributional misspecification. Simulation results suggest that the new power modeling method is unbiased and shows good performance with regard to root mean squared error and type I error rates for the predicted required sample sizes and predicted power rates, outperforming alternative approaches, such as the naïve approach of selecting a discrete selection of sample sizes with linear interpolation of power or simple logistic regression approaches. The MSPE appears to be a valuable tool to estimate power for models without an (asymptotic) analytical power estimation.

RELATIONSHIP BETWEEN MATERNAL NUTRITIONAL KNOWLEDGE AND CHILDREN GROWTH UNDER 5 YEARS OF AGE IN LAKHODAIR COMMUNITY, LAHORE, PAKISTAN

Nutrition knowledge is essential for the prevention of nutrition-related problems. Knowledge is a familiarity, awareness, or understanding of someone or something, such as facts, information, descriptions, or skills, which is acquired through experience or education by perceiving, discovering, or learning. Objective: To determine the relationship between maternal nutritional knowledge and children's growth under 5 years of age in the Lakhodair community, Lahore Pakistan. Methods: A quantitative correlational study design was used to conduct this study. The study duration was 4 months after IRB approval. This study was conducted in the Lakhodair community in Lahore, Pakistan. Study Population females (mothers) living in the Lakhodair community in Lahore, Punjab Pakistan. A simple random sampling technique was used for the selection of sample size. Results: Results revealed that all participants had breastfed their child. The majority (90%) of the participants didn’t rate their knowledge so it was compromised by their attitude problems and lack of awareness regarding maternal nutritional guidelines. More than 72% have no access to the resources.68% responded that child eating habits are not average depicting mal-nutrition. P value was less than 0.05 showing the significant relation of maternal knowledge with children's growth. Conclusion: It was concluded that maternal nutritional knowledge has a positive and direct relation with the child's growth. As the mother’s knowledge increases ultimately practices will be better and consequently, maternal nutritional knowledge and childcare practices will significantly impact the child's growth.

Exploring the Relationship between Data Sample Size and Traffic Flow Prediction Accuracy

Efficiently extracting and analyzing large urban traffic data, accurately predicting traffic conditions, and improving urban traffic management require careful selection of an appropriate data sample size. The suitable size of data sample assumes paramount importance in fostering sustainable transportation development. This paper investigates the relationship between traffic flow prediction performance and data sample size, considering data sample missing rates, duration, and road segment coverage. Real traffic flow data from 13 road sections in Changsha, China, are analyzed using the Decision Tree, Support Vector Machine, Gaussian Process Regression and Artificial Neural Network models. Some key findings include: Lower data sample loss rates improve prediction accuracy by capturing traffic flow patterns effectively, while higher loss rates decrease accuracy; an optimal data sample duration of around 7 days balances prediction accuracy and data stability, with longer durations providing more historical data but risking complexity; Broader road segment coverage gives a more comprehensive traffic flow information, but excessive coverage introduces noise and impacts the improvement of prediction accuracy. The results highlight the significant impact of data sample size on prediction performance. Enhancing reliability can be achieved by reducing data loss, selecting suitable durations, and considering appropriate road segment coverage, supporting improved traffic management and route planning.

Randomized Phase II Cancer Clinical Trials to Validate Predictive Biomarkers.

The design of cancer clinical trials incorporating biomarkers depends on various factors, including the trial phase, the type of biomarker and whether its role has been validated. This article aims to present a method for designing and analyzing phase II cancer clinical trials that validate predictive biomarkers. We propose a randomized trial design where patients are allocated between a targeted therapy and a non-targeted therapy stratified by biomarker status. Tumor response is used as the primary endpoint to validate the biomarker through interaction testing between treatment and biomarker positivity. Additionally we propose a sample size calculation method for this design, considering two types of interaction: one based on logit-transformed response rates and the other on raw response rates. The proposed sample size method is applied to the design of a real randomized phase II trial. Extensive simulations are conducted to evaluate the performance of the test statistic and the sample size method under different scenarios. Our method provides a practical approach to validating predictive biomarkers in phase II cancer trials. The simulations demonstrate robust performance for both interaction models, offering guidance for the sample size selection and analysis strategy in biomarker-stratified trials.

The density of the inner cell mass is a new indicator of the quality of a human blastocyst: a valid supplement to the Gardner scoring system.

Can the density of the inner cell mass (ICM) be a new indicator of the quality of the human blastocyst? The densification index (DI) developed in this study can quantify ICM density and provide positive guidance for ploidy, pregnancy, and live birth. In evaluating the quality of ICM, reproductive care clinics still use size indicators without further evaluation. The main disadvantage of this current method is that the evaluation of blastocyst ICM is relatively rough and cannot meet the needs of clinical embryologists, especially when multiple blastocysts have the same ICM score, which makes them difficult to evaluate further. This observational study included data from 2272 blastocysts in 1991 frozen-thawed embryo transfer (FET) cycles between January 2018 to November 2021 and 1105 blastocysts in 430 preimplantation genetic testing cycles between January 2019 and February 2023. FET, ICSI, blastocyst culture, trophectoderm biopsy, time-lapse (TL) monitoring, and next-generation sequencing were performed. After preliminary sample size selection, the 11 focal plane images captured by the TL system were normalized and the spatial frequency was used to construct the DI of the ICM. This study successfully constructed a quantitative indicator DI that can reflect the degree of ICM density in terms of fusion and texture features. The higher the DI value, the better the density of the blastocyst ICM, and the higher the chances that the blastocyst was euploid (P < 0.001) and that pregnancy (P < 0.001) and live birth (P = 0.005) were reached. In blastocysts with ICM graded B and blastocysts graded 4BB, DI was also positively associated with ploidy, pregnancy, and live birth (P < 0.05). ROC analysis showed that combining the Gardner scoring system with DI can more effectively predict pregnancy and live births, when compared to using the Gardner scoring system alone. Accurate calculation of the DI value places high demands on image quality, requiring manual selection of the clearest focal plane and exposure control. Images with the ICM not completely within the field of view cannot be used. The association between the density of ICM and chromosomal mosaicism was not evaluated. The associations between the density of ICM and different assisted reproductive technologies and different culture conditions in embryo laboratories were also not evaluated. Prospective studies are needed to further investigate the impact of ICM density on clinical outcomes. ICM density assessment is a new direction in blastocyst assessment. This study explores new ways of assessing blastocyst ICM density and develops quantitative indicators and a corresponding qualitative evaluation scheme for ICM density. The DI of the blastocyst ICM developed in this study is easy to calculate and requires only TL equipment and image processing, providing positive guidance for clinical outcomes. The qualitative evaluation scheme of ICM density can assist embryologists without TL equipment to manually evaluate ICM density. ICM density is a simple indicator that can be used in practice and is a good complement to the blastocyst scoring systems currently used in most centers. This work was supported by the National Key Research & Development Program of China (2021YFC2700603). The authors report no financial or commercial conflicts of interest. N/A.

Analysis of Economic Efficiency: Case of Sweet Potato Seed Vine Production in Homabay County, Kenya

Sweet potatoes are not only a nutritious, high value food for the daily diet, but also an important source of income. In the selected survey areas in the Lake Region of Kenya, sweet potato is amongst the major commercially grown crops. Therefore, in order to understand the profitability and performance of sweet potato producers in the study area, this study was implemented. Sweet potato is one of the main commercially produced crops in the selected survey areas in the lake region of Kenya. Therefore, this study was conducted to understand the profitability and performance of sweet potato producers in the study areas. The study employed Cost Function in the analyses of Economic Efficiency of Sweet Potato Seed Vine Production system in Kenya. A multi-stage random sampling technique was used to select sample size of 150 sweet potato vine producers for this study. By use of a structured questionnaire, data was collected on production input, output, input and output prices, characteristics of sampled farmer and plot. Using the maximum likelihood method, the variables of the cost function were estimated. Result revealed that the farm level cost- efficiency was about 56 percent. The research has shown that age and farm size were negative but significantly related to cost-efficiency at ten and one percent respectively. Gender, farming experience, Education, access extension to and credit were positive but significantly associated with cost-efficiency at one percent. The study advocates for policy decision that youthful farmers be inspired to venture into SP vine production and the government to enhance access to education, credit and extension services the ongoing farming.

Reverse Logistics and Performance of Plastic Bottling Firms in Mombasa County, Kenya

Purpose: The purpose of this study was to determine the relationship between reverse logistics and the performance of plastic bottling firms in Mombasa County, Kenya. Methodology: Cross-sectional research design was employed in the study. The study considered all 27 plastic bottling firms registered by Mombasa County Department of Trade. Stratified random sampling techniques was used to select sample size and Slovin’s formula was adopted to get the sample size for the study. Primary data was collected by use of structured questionnaires. Collected data was analyzed by employing descriptive and inferential statistics as the data analysis techniques. Descriptive statistics gives the profile of the respondents, that is, the frequencies and their percentages; whereas inferential statistics adopts a hierarchical, moderated, multiple regression analysis model in order to determine the effect of the explanatory variable. Statistical Package for Social Science was used as the data analysis tool. Data was presented in frequency and descriptive tables. Findings: The study established that there is a correlation of 0.748 between reverse logistics and performance of plastic bottling firms in Mombasa County. Reverse logistics accounted for 56.0% of the variation in the performance of plastic bottling firms in Mombasa County. Unique Contribution to Theory, Practice and Policy: Systems theory was adopted and validated. By incorporating linking the entire supply chain, Systems theory could assist plastic bottling firms in developing a seamless sustainable reverse logistics. The study revealed that successfully implementing reverse logistics; minimize unnecessary costs, protect the environment and increase firm returns.

Research on Nondestructive Inspection of Fruits Based on Spectroscopy Techniques: Experimental Scenarios, ROI, Number of Samples, and Number of Features

Spectral technology is a scientific method used to study and analyze substances. In recent years, the role of spectral technology in the non-destructive testing (NDT) of fruits has become increasingly important, and it is expected that its application in the NDT of fruits will be promoted in the coming years. However, there are still challenges in terms of dataset collection methods. This article aims to enhance the effectiveness of spectral technology in NDT of citrus and other fruits and to apply this technology in orchard environments. Firstly, the principles of spectral imaging systems and chemometric methods in spectral analysis are summarized. In addition, while collecting fruit samples, selecting an experimental environment is crucial for the study of maturity classification and pest detection. Subsequently, this article elaborates on the methods for selecting regions of interest (ROIs) for fruits in this field, considering both quantitative and qualitative perspectives. Finally, the impact of sample size and feature size selection on the experimental process is discussed, and the advantages and limitations of the current research are analyzed. Therefore, future research should focus on addressing the challenges of spectroscopy techniques in the non-destructive inspection of citrus and other fruits to improve the accuracy and stability of the inspection process. At the same time, achieving the collection of spectral data of citrus samples in orchard environments, efficiently selecting regions of interest, scientifically selecting sample and feature quantities, and optimizing the entire dataset collection process are critical future research directions. Such efforts will help to improve the application efficiency of spectral technology in the fruit industry and provide broad opportunities for further research.

How Much Radiation Are Women in Saudi Arabia Receiving from Mammography? A Review

This review compiles and assesses data from recent studies on mammographic radiation doses in Saudi Arabia, aiming to evaluate mean glandular dose (MGD) exposure during mammography and its implications in breast cancer risk. The reviewed studies spanned from 2019 to 2023 and included a range of sample sizes and institutional settings, with patients’ ages from 27 to 85 years. Considerations such as the number of mammographic views and compressed breast thickness were examined. The studies reported average MGDs below the National Diagnostic Reference Level set by the Saudi Food and Drug Authority. However, limitations were noted regarding sample size selection and incomplete data on all mammographic projections. Despite these limitations, the findings highlight the need for continued assessment of patient doses to optimize mammography practices and address the absence of quality standardization acts in Saudi Arabia. These insights are critical for governing authorities to ensure that effective patient dose monitoring occurs regularly and that the establishment of minimum quality standards for breast cancer screening is intact.

Role of Time Management in Developing Self- Regulating Study Habits among Students at University Level

The research work conducted on the topic of role of time management in developing self-regulating study habits among students at University level. In order to achieve the objectives, this study used descriptive survey design. Total population was 792 students from public sector universities of okara region. 70% sample of total population was selected. Consequently, 167 male and 217 female students were selected from University of Okara. Whereas, 91 male 80 female students were selected from the University of Agriculture Faisalabad ( sub campus Depalpur Okara). All of these students were selected from various departments (Statistics, Islamic Studies, Physics, Mathematics, Education, Biology, English, Zoology, Chemistry, and Computer Science). A self-structured questionnaire was used to collect data for the students. It was a 5-point scale type consisting 40 statements along with 2 questions as open ended to assess the role of time management in developing self-regulating study habits among students at university level. Systematic random sampling method was used to select sample size of 555 students. The purpose of this study was to achieve the following objectives, 1 assess the effect of time management on self-regulating study habits of students at university level, 2 to analyze the important factors that affect the study habits of students at university level, 3 to compare the effect of time management techniques on the basis of gender, locality, university and departments (science and arts). Present study was aimed to answer the following questions, 1) how the time management effect self-regulating study habits of students at university level? 2) What are the factors that affect the study habits of students at university level? 3) What is the effect of time management techniques on the basis of gender, locality, university and departments (science and arts)? The results help in the field of education specially and for the bright future of students generally. The major findings of this study indicate that majority of the students have agreed about the importance of time management for achieving high ranks at university level. They feel stressed due to over work and try to minimize interruptions. Most of the students have reported that they share their academic issues with family and home environment affect the students grades. Students also reported that class environment also affects student learning. The study concluded that importance of time management can never be neglected. It indicates that time management is a key to success and is very important for the development of self-regulating study habits among students at university level. The study also concluded that there is no significant difference between the students on the basis of gender, locality, university and science and arts departments regarding the role of time management in developing self-regulating study habits among students at university level.

Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.

Large language models (LLMs) have the potential to support promising new applications in health informatics. However, practical data on sample size considerations for fine-tuning LLMs to perform specific tasks in biomedical and health policy contexts are lacking. This study aims to evaluate sample size and sample selection techniques for fine-tuning LLMs to support improved named entity recognition (NER) for a custom data set of conflicts of interest disclosure statements. A random sample of 200 disclosure statements was prepared for annotation. All "PERSON" and "ORG" entities were identified by each of the 2 raters, and once appropriate agreement was established, the annotators independently annotated an additional 290 disclosure statements. From the 490 annotated documents, 2500 stratified random samples in different size ranges were drawn. The 2500 training set subsamples were used to fine-tune a selection of language models across 2 model architectures (Bidirectional Encoder Representations from Transformers [BERT] and Generative Pre-trained Transformer [GPT]) for improved NER, and multiple regression was used to assess the relationship between sample size (sentences), entity density (entities per sentence [EPS]), and trained model performance (F1-score). Additionally, single-predictor threshold regression models were used to evaluate the possibility of diminishing marginal returns from increased sample size or entity density. Fine-tuned models ranged in topline NER performance from F1-score=0.79 to F1-score=0.96 across architectures. Two-predictor multiple linear regression models were statistically significant with multiple R2 ranging from 0.6057 to 0.7896 (all P<.001). EPS and the number of sentences were significant predictors of F1-scores in all cases ( P<.001), except for the GPT-2_large model, where EPS was not a significant predictor (P=.184). Model thresholds indicate points of diminishing marginal return from increased training data set sample size measured by the number of sentences, with point estimates ranging from 439 sentences for RoBERTa_large to 527 sentences for GPT-2_large. Likewise, the threshold regression models indicate a diminishing marginal return for EPS with point estimates between 1.36 and 1.38. Relatively modest sample sizes can be used to fine-tune LLMs for NER tasks applied to biomedical text, and training data entity density should representatively approximate entity density in production data. Training data quality and a model architecture's intended use (text generation vs text processing or classification) may be as, or more, important as training data volume and model parameter size.

Reappraising the psychosomatic approach in the study of "chronic orofacial pain": looking for the essential nature of these intractable conditions.

As burning mouth syndrome (BMS) and atypical odontalgia (AO) continue to remain complex in terms of pathophysiology and lack explicit treatment protocol, clinicians are left searching for appropriate solutions. Oversimplification solves nothing about what bothers us in clinical situations with BMS or AO. It is important to treat a complicated phenomenon as complex. We should keep careful observations and fact-finding based on a pragmatic approach toward drug selection and prescription with regular follow-up. We also need to assess the long-term prognosis of treatment with a meticulous selection of sample size and characteristics. Further investigation of BMS and AO from a psychosomatic perspective has the potential to provide new insight into the interface between brain function and "chronic orofacial pain."

Family Issues on Effectiveness of Substance Abuse Preventive Measures among Secondary Schools in Kakamega County, Kenya

The rising prevalence of youth involvement in substance abuse is a global issue that requires immediate attention.The critical need everywhere in the 21 century world is to prepare students to lead healthy lives by providing them with relevant educational programmes inclusive of family matters. The study examined influence of family issues on effectiveness of substance abuse preventive measures among secondary school students in Kakamega County through mixed design. The target population was 59675 form three students, 1080 class teachers, 530 G/C teachers and 12 parent representatives in the study area. Multi-stage sampling, simple random and purposive sampling techniques were used to select sample size; 381 students, 108 class teachers, 53 G/C and 12 parent representatives. The study adopted a cross-sectional survey design.Structured questionnaires, focus group discussion guide and interview guide were used to collect data which was subjected to descriptive statistics, correlation and simple linear regression. Qualitative data was transcribed, analysed and reported according to emerging themes. Supervisors were consulted to determine the face, content, and construct validity of the instruments while reliability was tested using split half method and Cronbach’s alpha of reliability coefficient index for data collection instruments was above 0.8 both for teachers and students. The study established that there was a strong positive association (r=.730, p&lt;.01, N=270; r=.768, p&lt;.01, N=59) between influence of family issues and effectiveness of substance abuse preventive interventions. The study further established that family issues significantly influence effectiveness of substance abuse preventive measures, thus, [F (1, 269) = 415.202, P&lt;.05] and where [F (1, 58) = 24.071, P&lt;.05]. The study concludes that family issues significantly influence effectiveness of substance abuse preventive measures. Based on conclusion, it was therefore recommended that schools should increase forums on parent-child communication so as to work on role modeling as an effective target in substance abuse preventive measures.

Treatment Effects of Introducing the Neurosequential Model of Therapeutics in a Norwegian Residential Treatment Facility for Children Aged 7-13.

This study investigates the impact of the Neurosequential Model of Therapeutics (NMT) in child and adolescent psychiatric care, addressing a gap in current clinical methodologies that tend to focus on single problems rather than the interconnected nature of many real-life mental health issues. The study was conducted in a residential setting over an extended period, including children aged 7-13, to observe the effects of implementing NMT. The children presented with complex symptoms and multiple diagnoses. The methods incorporated the NMT approach, emphasizing individualized treatment plans based on each child's unique brain development, and aimed at addressing multiple, interconnected problems simultaneously. Results from multilevel model analyses of behavioral difficulties, measured using the Child Behavior Checklist (CBCL), revealed substantial improvements in treatment effectiveness post-NMT implementation. Despite the limitations, such as a non-randomized participant selection and limited sample size, the findings strongly suggest that NMT enhances care effectiveness in real-world clinical settings, particularly for children with complex mental health issues. The study concludes that relationally oriented milieu therapy, and specifically the NMT approach, holds great promise for advancing pediatric psychiatric care, advocating for its broader application and further research to refine and substantiate its efficacy.

National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France

Accurate soil property and class predictions through spatial modelling necessitate a thoughtful selection of explanatory variables and sample size, as their choice greatly impacts model performance. Within the framework of Global Soil Nutrient and Nutrient Budget maps (GSNmap), the FAO Global Soil Partnership (GSP) launched a country-driven digital soil mapping (DSM) approach. The GSP asked the countries if they could implement the DSM prediction of ten soil properties, using their national point data and a set of widely available covariates (GSP_Cov). In this study, we examined the effect of including additional national-based covariates and soil observations on the performance of the prediction models using mainland France as a pilot. The learning soil dataset was based on a systematic 16-to-16 km grid. For a subset of soil properties, we also assessed using repeated k-fold cross-validation the effect of adding to this dataset many other irregularly spread measurements. The GSP_Cov included common widely available covariates that represented information about terrain, climate, and organisms. The second set of covariates consisted of the GSP_Cov, extended to extra covariates available at a national level, such as previously existing soil maps, geological maps, remote sensing products and others. Random Forest approach in combination with the Boruta selection method was employed for mapping ten soil properties: soil organic carbon (SOC), pH (water), total nitrogen (N), available phosphorus (P), available potassium (K), cation exchange capacity (CEC), bulk density (BD), and texture (clay, silt, and sand). The results revealed noteworthy enhancements in prediction performance for more than half of the properties, although, for some of them, the improvements were negligible. The most significant improvements were obtained for pH, CEC and texture, where geological variables and a previous pH map significantly contributed to the increase in accuracy. Adding numerous points (around 25,000) to the learning dataset improved the performance of soil particle-size fractions predictions. By broadening the spectrum of covariates and better covering the feature and geographical spaces considered in soil prediction models, this research underscores the importance of implementing a more diverse range of covariates at a national scale and of densifying soil information to enlarge the feature and geographical spaces of multidimensional soil/covariates combinations. This information should be taken into account in national and continental digital soil mapping endeavours.

Correcting for collider effects and sample selection bias in psychological research.

Colliders, variables that serve as a common outcome of an independent and dependent variable, pose a major challenge in psychological research. Collider variables can induce bias in the estimation of a population relationship of interest when (a) the composition of a research sample is restricted by scores on a collider variable or (b) researchers adjust for a collider variable in their statistical analyses, as they might do for confounder variables. Both cases interfere with the accuracy and generalizability of statistical results. Despite their importance, however, collider effects remain relatively unknown in psychology. This tutorial article summarizes both the conceptual and the mathematical foundation for collider effects and their relevance to psychological research, and then proposes a method to correct for collider bias in cases of restrictive sample selection based on Thorndike's Case III adjustment (1982). Two simulation studies demonstrated Thorndike's correction as a viable solution for correcting collider bias in research studies, even when restriction on the collider variable was extreme and the selected sample size was as low as N = 100. Bias and relative bias results are reported to evaluate how well the correction equation approximates targeted population correlations under a variety of parameter conditions. We illustrate the application of the correction method to a hypothetical study of intelligence and conscientiousness, discuss the applicability of the method to more complex statistical models as a means of detection for collider bias, and provide code for researchers to apply to their own research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

IJCM_212A: Assessment of Effective Vaccine Management in Government Health Facilities of Jammu district

Background: The success of immunization depends highly on the level of cold chain maintenance. For effective implementation and success of the Universal Immunisation Programme, proper maintenance of Cold Chain needs a great focus. In 2010, WHO and UNICEF launched the Effective Vaccine Management (EVM) initiative. EVM is a tool which assesses immunization supply chain logistics management. Objectives: To evaluate the performance of Immunization supply chain in cold chain points of Government Health Facilities (HF) of Jammu district Methodology: A facility based cross-sectional study was conducted in Jammu district of UT of J&amp;K. The assessment was done during November 2021– October 2022 using WHO-UNICEF EVM assessment standalone questionnaire generated on EVM assessor app. Sample size and site selection were done using the WHO EVM site selection tool(v1.7-March 2014). A total of 31 HFs were assessed including Divisional Vaccine Store (SN) Results: None of the health facilities reached the WHO recommended overall score of &gt;80%. The criterion scores showed that Facility infrastructure and equipment (E4) and waste management (E9) for District Vaccine Store (LD) was 83% &amp; 86%. Vaccine management (E8) score for SN level &amp; Government Hospital was 88% &amp; 82%. Rest of the CCPs scored less than 80% for all the criteria(E2-E9). The scores of distribution of vaccines and dry goods (E7) were very low for Primary Health Centres (PHC) and Health &amp; Wellness Centres (H&amp;WC) i.e., 39% and 25%. The scores for all the categories(C1-C6) for various CCPs were below 80%. Management criteria scores for supportive supervision(M3) was 0% for all CCP except LD &amp;PHC Conclusion: The overall performance of cold chain in Jammu district was below the recommended level. Supportive supervision, provision of safe disposal pits, power backup, micro plan, training of Cold Chain Handlers and provision of vaccine transportation facility can help in effective vaccine management