Selection Process Research Articles

Agronomic Evaluation and Chemical Characterization of Salvia lavandulifolia Vahl. over 3 Consecutive Years Cultivated Under Harsh Climatic Conditions in Southeast Spain

The cultivation of Salvia lavandulifolia, Spanish sage, makes an important contribution to the economy of many rural areas in Southeastern Spain. This aromatic plant species is characterized by high intraspecific variability, which makes the selection process for the establishment of homogeneous crops difficult. Additionally, imminent climate change threatens to reduce its production, especially when cultivated in drylands. Therefore, to guarantee the continued production of this type of sage, it is essential to study its agronomic behavior and production quality. For this, clones from four ecotypes were cultivated for three years, assessing changes in their biomass production, essential oil yield and quality, and phenolic fraction, as well as the corresponding antioxidant activity. The results suggest that essential oil yield is genetically predetermined, greater biomass not being associated with higher quantities of essential oil. Weather conditions affected both essential oil and phenolic fraction secondary metabolism. Under very harsh conditions, Spanish sage produces higher concentrations of camphor and 1,-8-cineole along with luteolin-7-O-glucoside, and lithospermic, rosmarinic, and salvianolic A acids in its phenolic fraction. The synthesis of these components helps the species to withstand the hot and dry conditions typical of southeast Spain.

Childhood socioeconomic status and depression in adulthood: systematic review

Abstract Background Low socioeconomic status (SES) is generally associated with poorer mental health, particularly depression. We carried out a systematic review to address the question of whether childhood SES is associated with depression in adulthood and the potential pathways which may explain this association. Methods We followed a systematic search strategy in PubMed and Scopus with specific inclusion and exclusion criteria. After removing duplicates, studies were screened for eligibility and quality was assessed independently by two assessors using the Newcastle-Ottawa scale. Results After the selection process, 20 observational studies (7 cohort and 13 cross-sectional) that examine the association between SES during childhood and depression in adulthood were included in the systematic review. The most common measures of childhood SES were parental education, occupation or family income, estimated subjectively and/or objectively. In 17 of the studies an inverse relationship between childhood SES and depression was shown. In 17 studies, models were employed to demonstrate the possible pathways which explain the relationship between childhood SES and depressive symptoms. The most important mediators were adult SES, psychosocial factors, childhood trauma and physical health. Conclusions Our findings reveal the long-term association between childhood socioeconomic conditions and depression later in life. Suboptimal childhood SES seems to be an important risk factor for depression in adulthood. Improving childhood socioeconomic conditions can potentially have lasting effects well beyond childhood. Key messages • Protecting children against the effects of socioeconomic adversity could reduce depression in adult life. • Policy makers should direct resources towards alleviating childhood disadvantage for promoting health in adulthood.

The use of digital technologies to promote sexual health in young adults: a narrative review

Abstract Background New technologies have the potential to play an important role in promoting sexual health, especially among young people, who are the biggest users and those at greatest risk of contracting Sexually Transmitted Infections (STI). The risk behavior assumed by young people today highlights this vulnerability in several contexts, such as individual, social, economic, and family. In this sense, it becomes relevant to approach sex education in schools using digital technologies. Objectives To identify the evidence in the literature on the use of digital technologies to promote the sexual health of young adults. Methods Narrative literature review. Three independent reviewers extracted, analyzed and synthesized the articles and results. Results Through the selection process, four studies were included. The articles show that the use of digital technologies (mobile phones/applications) leads to increased knowledge, improved attitudes and sexual behavior among young adults. The interventions studied in the studies were based on Short Message Service, electronic mail interventions and the MyPEEPS mobile application, with the following main focuses: Increased knowledge, attitudes, and behaviors of young adults. Considering the results of the studies, all interventions proved to be effective as facilitators of sexual health promotion. Conclusions The use of digital technologies allowed an increase in the promotion of the sexual health of the participants, since there was an increase in the level of knowledge with the use of digital technologies such as SMS, emails and mobile applications when comparing the experimental and control groups. The use of these digital technologies also allowed for a change in attitudes and behaviors, such as a significant increase in serological testing for STIs and an increase in health care seeking. Key messages • The use of digital technology promotes the sexual health of young adults, so these strategies should be implemented by health professionals. • The dissemination of these digital strategies will have an impact on Public Health, specifically regarding the sexual health of young adults.

If you fit, you commit? The relationship between the organizational climate and employees’ commitment: the moderating role of the person-environment fit

PurposeBased on the theory of organizational socialization, this article broadens the knowledge in the field of organizational commitment by determining the relationship between the organizational climate (OC) and the employees’ commitment, as well as the moderating role of the person-environment (P-E) fit.Design/methodology/approachWe conducted quantitative research using three psychometric questionnaires. We investigated a large sample (N = 1,032) of employees hired in Poland.FindingsWe found strong relationships between the OC, the employees’ fit and their commitment. Moreover, both supplementary and complementary fit significantly moderated the relationships between the majority of climate dimensions and, especially, affective commitment. Interestingly, highly fitted employees with longer tenure showed a stronger relationship between material climate dimensions and commitment compared to similarly fitted newcomers, for whom the most important were relationships with co-workers and superiors.Research limitations/implicationsWe analyzed only a subjective fit among employees working in Poland. Although it was beneficial for developing the OC knowledge of non-American sample, the results require cautious generalization.Practical implicationsAssessing a candidate’s fit with the organization through detailed interviews, behavioral questions or practical tasks during the selection process can improve candidates' and employees’ P-E fit. A better fit can increase commitment, even if the OC or other factors are not perfect. Socialization tactics aimed at strengthening the fit can facilitate better alignment with the climate and higher commitment among employees with longer tenure.Originality/valueThis study is the first to empirically verify the moderating role of the P-E fit on the relations between OC and organizational commitment. It also considers the comparison between more experienced employees and newcomers.

Kinematic Analysis of Elite Indian National-level Sprinters: a Comprehensive Analysis

A comprehensive research study was undertaken involving ten national-level sprinters selected purposively from the athletics ground of Jawaharlal Nehru Stadium in New Delhi. The primary objective was to identify the key angles influencing movement during predefined steps in sprinting. The age range of the participants was 19 to 27 years, and the selection process followed purposive sampling. Descriptive statistics were employed to interpret the collected data, providing a summary of the sprinters' performances. The study considered various biomechanical aspects, including angles related to knee movement, hip articulation, and other relevant variables. The correlation between angular and linear variables was explored, and KINOVEA software was utilized for precise angle measurements, ensuring high accuracy in recording sprinters' movements. This technological approach facilitated a thorough examination of the kinematic aspects of their performance. Data collection employed a judgmental sampling method, and the results revealed significant relationships. Notably, there was a significant correlation between the angle at the right ankle joint and the angle at the right wrist joint at the 1st step. Additionally, a significant correlation was observed at the right knee joint at the 3rd step, indicating its contribution to the athletes' overall performance.

The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization.

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

Rancangan Sistem Pendukung Keputusan Pemilihan Calon Pegawai Honorer Pemerintah Kabupaten Lamandau Menggunakan Metode Profile Matching

This study aims to design a Decision Support System (DSS) that implements the Profile Matchingmethod for the selection of honorary employees in the Lamandau District Government. The oftennon-systematic selection process results in less objective decisions, necessitating a system that canenhance effectiveness and transparency in decision-making. The Profile Matching method is usedto compare the profiles of candidates with predetermined criteria, leading to a fairer and moreaccurate selection process. Data were collected through interviews with relevant stakeholders inthe Lamandau District Government, which were then analyzed to identify appropriate selectioncriteria. The findings indicate that the application of DSS can reduce subjectivity and improveclarity in the evaluation process. Additionally, the designed computer-based system facilitatesfaster data processing and presents information in a more comprehensible manner. Thus, thisresearch is expected to make a significant contribution to the development of employee selectionsystems in local government and enhance the quality of human resources in public service. Theimplementation of information technology in DSS is anticipated to serve as a reference for futureresearch in decision-making and human resource management.

The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

Footprints of innate immune activity during HIV-1 reservoir cell evolution in early-treated infection.

Antiretroviral treatment (ART) initiation during the early stages of HIV-1 infection is associated with a higher probability of maintaining drug-free viral control during subsequent treatment interruptions, for reasons that remain unclear. Using samples from a randomized-controlled human clinical trial evaluating therapeutic HIV-1 vaccines, we here show that early ART commencement is frequently associated with accelerated and efficient selection of genome-intact HIV-1 proviruses in repressive chromatin locations during the first year after treatment initiation. This selection process was unaffected by vaccine-induced HIV-1-specific T cell responses. Single-cell proteogenomic profiling demonstrated that cells harboring intact HIV-1 displayed a discrete phenotypic signature of immune selection by innate immune responses, characterized by a slight but significant upregulation of HLA-C, HLA-G, the IL-10 receptor, and other markers involved in innate immune regulation. Together, these results suggest an accelerated immune selection of viral reservoir cells during early-treated HIV-1 infection that seems at least partially driven by innate immune responses.

Football coach's strategy in preventing aggressive behavior in adolescents

This study aims to review the types of strategies that football coaches employ to prevent aggressive behavior in adolescents. Using the systematic literature review, data were obtained from articles searched through Google Scholar. The search focused on those published in Scopus-indexed journals published between 2019 and 2024. The keywords involved "Strategy And Soccer Coaches And Prevention And Aggressive Behavior And Teenagers". The search generated 15,400 articles as initial results. To be included in the review, inclusion and exclusion criteria were applied. The articles were screened again using these criteria, and 19 articles that fit the theme and met the inclusion requirements were yielded. The process of the article selection in this study followed the PRISMA guidelines. The main findings of this study emphasized several factors that play a key role in creating a supportive environment for young athletes. The first factor was emotional education. Teaching athletes to recognize and manage their emotions helped football coaches reduce athletes’ aggressive reactions, especially during the pressure of competition. In addition, open and consistent communication between soccer coaches and athletes was reported to reinforce positive interpersonal relationships, which in turn lowered levels of aggressiveness on the field. Finally, building a team culture that emphasizes the values of cooperation and mutual respect has also been proven effective in preventing aggressive behavior. Therefore, soccer coaches have a great responsibility not only to develop athletes' technical skills but also to shape their character through an integrative and sustainable approach.

Towards ML Models’ Recommendations

AbstractArtificial Intelligence encompasses a range of technologies that replicate human-like cognitive abilities through computer systems, enabling the execution of tasks associated with intelligent beings. A prominent way to achieve this is machine learning (ML), which optimizes system performance by employing learning algorithms to create models based on data and its inherent patterns. Today, a multitude of ML models exist having diverse characteristics, including the algorithm type, training dataset, and resultant performance. Such diversity complicates the selection of an appropriate model for a specific use case, answering user demands. This paper presents an approach for ML models retrieval based on the matching between user inputs and ML models criteria, all described in a semantic ML ontology named SML model (Semantic Machine Learning model), which facilitates the process of ML models selection. Our approach is based on similarities measures that we tested and experimented to score the ML models and retrieve the ones matching, at best, user inputs.

Evaluation and Optimization of Cement Slurry Systems for Ultra-Deep Well Cementing at 220 °C

With the depletion of shallow oil and gas resources, wells are being drilled to deeper and deeper depths to find new hydrocarbon reserves. This study presents the selection and optimization process of the cement slurries to be used for the deepest well ever drilled in China, with a planned vertical depth of 11,100 m. The bottomhole circulating and static temperatures of the well were estimated to be 210 °C and 220 °C, respectively, while the bottomhole pressure was estimated to be 130 MPa. Laboratory tests simulating the bottomhole conditions were conducted to evaluate and compare the slurry formulations supplied by four different service providers. Test results indicated that the inappropriate use of a stirred fluid loss testing apparatus could lead to overdesign of the fluid loss properties of the cement slurry, which could, in turn, lead to abnormal gelation of the cement slurry during thickening time tests. The initial formulation given by different service providers could meet most of the design requirements, except for the long-term strength stability. The combined addition of crystalline silica and a reactive aluminum-bearing compound to oil well cement is critical for preventing microstructure coarsening and strength retrogression at 220 °C. Two of the finally optimized cement slurry formulations had thickening times more than 4 h, API fluid loss values less than 50 mL, sedimentation stability better than 0.02 g/cm3, and compressive strengths higher than 30 MPa during the curing period from 1 d to 30 d.

Quality indicators of long-term care for older people in OECD countries; a rapid scoping review

Abstract Background The COVID-19 pandemic has exposed the vulnerability of older people living in long-term care facilities (LTCF), highlighting the urgent need for research on the quality of care for this group of the population. There is no universal definition for quality of care in LTCF, and yet, multiple tools and indicators have been developed to measure it. In this rapid scoping review, we aim to provide a comprehensive overview of the current knowledge on quality of care concepts and indicators for older people in LTCF in OECD countries. Methods This review follows the World Health Organization (WHO) guide for rapid reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) extension for scoping reviews, including protocol registration (https://doi.org/10.17605/OSF.IO/34B2S). The electronic bibliographic databases MEDLINE (PubMed), EMBASE, and CINAHL were searched in September 2023 for studies published between 2013 and 2023 for quality indicators in LTCF. Experts in quality of care in LTCF were contacted and consulted to retrieve sources of grey literature. Information on the definition or framework of quality of care, quality of care dimensions, measurement instruments, context and purpose of the measurement will be extracted. Results Of the 8,516 identified records, 6,449 were screened by title and abstract after removing duplicates. A total of 469 publications will be screened in full-text. Experts recommended 40 websites to be screened for quality indicators. The PRISMA flowchart diagram will be provided to describe the selection process. Conclusions This review aims to improve the quality of care for older people living in LTCF. Our results will identify which concepts and dimensions of care are currently being measured in OECD countries, whether they are backed by any definition and scientific framework of quality of care for older residents of LTCF, and whether there is consideration of closing gaps in quality measurement. Key messages • Quality of care is an understudied field in the context of long-term care facilities for older people. • In order to further improve quality in long-term care for older people, it is necessary to systematically review the current state of knowledge on quality concepts and indicators.

Prevalence of metabolic dysfunction-associated steatotic liver disease in people living with HIV and on antiretroviral treatment: A systematic review and meta-analysis protocol.

Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most common hepatic condition globally. The prevalence of MASLD continues to increase, paralleling the consistent rising rates of risk factors such as obesity and type 2 diabetes. Literature suggests that human immunodeficiency virus-infected (HIV-infected) individuals may have an increased risk of developing MASLD due to a complex interplay of factors including antiretroviral therapy. Since the development and widespread use of effective antiretroviral therapy (ART), HIV-induced liver disease has continued to be the predominant cause of liver-related morbidity and mortality. This protocol serves to narrate the methods that will be employed in conducting the published literature search for the systematic review and meta-analysis which will report on the global prevalence of MASLD on people living with HIV and on ARV treatment. The search of literature will be done using search engines or electronic databases including PubMed, Google Scholar, African Journal Online, and ResearchGate. Specific keywords will be used to search literature that has reported on the prevalence of MASLD among HIV patients receiving antiretroviral treatment, this will ensure the reproducibility of the study. Cross-sectional and longitudinal observational studies, retrospective cohort studies, clinical trial studies, meta-analyses, and systematic reviews that were published in the English language from 1990 to 2024 will be included. Animal studies will be excluded. Three independent reviewers will conduct the selection process and select studies that meet the eligibility criteria. A quality assessment tool, Downs and Blacks will be used to assess the risk of bias of the selected studies. A review manager will be used for meta-analysis of collected data and the Grading of Recommendations Assessment, Development, and Evaluation tool will assess the strength of evidence. The review will not require ethical clearance as it will only include data that is publicly available in published reports. The results of this review will be disseminated through publications. This study is registered with PROSPERO (CRD42024516814).

Implementasi Algoritma Multi-Factor Evaluation Process Pada Website Sistem Pendukung Keputusan Minat Siswa

After graduating from high school, students often have difficulty in determining the right major.The challenges faced include the lack of clear guidelines and criteria in choosing a major, as wellas the selection process which is still traditional and subjective. To overcome these obstacles, thisarticle suggests the implementation of a website-based Decision Support System (DSS) using theMulti-Factor Evaluation Process (MFEP) algorithm. This DSS was developed to help studentschoose a major that suits their interests and potential. The methods used in this study includeliterature studies, observations, interviews, and questionnaires. The results of the study indicatethat the use of a web-based DSS with the MFEP algorithm is able to provide more effective andobjective recommendations in helping students choose a major.

Significant pre-post effects on a patient-reported outcome measure in inpatient rehabilitation

Abstract Background We conducted a pilot study for the Swiss National Association for Quality Development in Hospitals and Clinics (ANQ) in the inpatient rehabilitation setting. The aim was to assess the suitability of a generic health-related quality of life patient-reported outcome measure (PROM) as an outcome quality indicator for public reporting across all rehabilitation domains on a national level. Methods In a multi-stage expert-based selection process, the PROMIS Global Health 10 was selected for piloting. We collected data from 29 rehabilitation clinics from April to December 2023. Mean T-scores and effect sizes were analysed across different rehabilitation domains. Patients’ need for assistance in completing the questionnaire was also assessed. Results A dataset of 2261 cases yielded a cleaned sample of 2083, with 1217 complete cases. Mean T-scores for physical health (PHS) and mental health (MHS) significantly differed (p < 0.001) at admission and discharge across all rehabilitation domains. The largest effects were observed in PHS for pulmonary (38.9(7.3); 46.0(7.6); dz = 0.95) and cardiologic rehabilitation (41.7(7.4); 47.9(7.2); dz = 0.85), and in MHS for internal medicine (44.1(6.9); 49.1(5.6); dz = 0.80) and psychosomatic rehabilitation (36.0(7.2); 45.2(8.2); dz = 1.19), while the smallest effects were noted in PHS for paraplegia (36.3(7.2); 39.8(7.2); dz = 0.50) and in MHS for the musculoskeletal rehabilitation domain (46.2(7.9); 48.7(7.4); dz = 0.33). Of all complete cases, 40.5% needed assistance in filling in the PROM and support needs were distributed unequally across rehabilitation domains, ranging from 10.0% in psychosomatic to 70.3% in geriatric rehabilitation. Conclusions The study presents a nuanced understanding of HRQOL across various rehabilitation domains, with notable effects observed across all domains, alongside significant assistance needs. Key messages • PROMIS-GH10 shows significant pre-post effects on physical and mental health in Swiss inpatient rehabilitation, with notable effects observed across all domains. • Observable barriers in implementation are evident due to substantial assistance requirements.

APPLIED LINGUISTICS WITH DEEP LEARNING-BASED DATA-DRIVEN TEXT-TO-SPEECH SYNTHESIZER FOR ARABIC CORPUS

The field of Applied Linguistics, which deals with language and its practical uses, connects with technology in interesting methods, especially in the advancement of text-to-speech (TTS) synthesizers. TTS synthesizers change written text into spoken words, deploying ethics from phonology, phonetics, and syntax to create natural-sounding speech. Within linguistics use, these methods are invaluable purposes such as increasing communication in fractal human–computer interactions (HCIs), language-learning tools, and providing accessibility solutions for visually impaired individuals. TTS purposes at synthesizing understandable and natural speech from text, and it has advanced quickly in recent times because of the progress of artificial intelligence (AI). During past years, deep learning (DL)-based TTS techniques have been established quickly, enabling the generation of natural speech with a high-quality narrator that matches human levels. Creating TTS methods at the quality of the human level has always been the aspiration of speech synthesis practitioners. Although current TTS techniques achieve impressive voice quality, there remains an evident gap in quality compared to human recordings. In this paper, we present an Applied Linguistics with Deep Learning-based Data-Driven Text-to-Speech Synthesizer (ALDL-DDTTS) technique for Arabic corpus. The ALDL-DDTTS technique mainly aims to detect the text and convert it into speech signals on Arabic corpora. In the ALDL-DDTTS technique, a multi-head attention bi-directional long short-term memory (MHA-BiLSTM) approach can be employed with fractal optimization methods to predict the diacritic and gemination signs. Additionally, the Buckwalter code has been deployed for capturing, storing, and displaying the Arabic texts. To boost the efficiency of the ALDL-DDTTS technique, the hyperparameter selection process uses the fractal ant lion optimization (ALO) algorithm. For examining the boost performance of the ALDL-DDTTS methodology, a wide range of simulations is involved. The experimental outcomes illustrated that the ALDL-DDTTS technique reaches better performance than other models.

An ensemble machine learning approach for predicting heart failure risk in acute coronary syndrome patients: heartguardai

Abstract Background Heart failure (HF) constitutes a major post-event morbidity in patients experiencing Acute Coronary Syndrome (ACS), underscoring the imperative for precise risk stratification methodologies. Traditional prognostic models, although beneficial, are limited by their reliance on a narrow scope of variables, thereby overlooking the comprehensive analytical potential afforded by extensive clinical datasets. Aims To develop "HeartGuardAI," an ensemble machine learning model that predicts HF risk in ACS patients. By incorporating a wide array of variables including ejection fraction (EF), glomerular filtration rate (GFR), body mas index(BMI), male sex, this model seeks to offer a more practical tool for clinical decision-making. Methods We queried ACTION-ACS a large-scale multicenter database of 3335 patients at 10-year follow-up. We employed a feature selection process informed by Random Forest Classifier to identify the most predictive variables. An ensemble model combining the strengths of Random Forest and Gradient Boosting methodologies was then developed. The model's predictive performance was validated through Stratified K-Fold cross-validation, focusing on Area Under the Curve (AUC) and confidence intervals to assess its efficacy. The model's predictive performance was validated through Stratified K-Fold cross-validation, focusing on Area Under the Curve (AUC) and confidence intervals to assess its efficacy. Results Demonstrating a mean AUC of 0.76 ± 0.036, "HeartGuardAI" showed a high capability to predict HF risk in post-ACS patients and emphasizes the significance of incorporating multifactorial clinical assessments Conclusions HF risk can be predicted by a handy tool developed with advanced machine learning methods. Future studies are needed to prospectively validate this algorithm.Graphic 1Graphic 2

Selection processes, transportability, and failure time analysis in life history studies.

In life history analysis of data from cohort studies, it is important to address the process by which participants are identified and selected. Many health studies select or enrol individuals based on whether they have experienced certain health related events, for example, disease diagnosis or some complication from disease. Standard methods of analysis rely on assumptions concerning the independence of selection and a person's prospective life history process, given their prior history. Violations of such assumptions are common, however, and can bias estimation of process features. This has implications for the internal and external validity of cohort studies, and for the transportabilty of results to a population. In this paper, we study failure time analysis by proposing a joint model for the cohort selection process and the failure process of interest. This allows us to address both independence assumptions and the transportability of study results. It is shown that transportability cannot be guaranteed in the absence of auxiliary information on the population. Conditions that produce dependent selection and types of auxiliary data are discussed and illustrated in numerical studies. The proposed framework is applied to a study of the risk of psoriatic arthritis in persons with psoriasis.

The Role of Recruitment and Selection on Organisational Performance: A Case of National Housing Corporation in Tanzania

This study examines the role of recruitment and selection practices on organisational performance at Tanzania's National Housing Corporation (NHC). Employing a descriptive research design, the study targeted all 76 employees at NHC headquarters across various departments, using census sampling to ensure comprehensive data collection and eliminate bias. Data were gathered through structured questionnaires with closed-ended questions measured on a five-point Likert scale. Analysis involved both descriptive statistics (percentages, frequency distributions, mean, and standard deviations) and inferential statistics (correlation) to examine relationships between independent variables (recruitment and selection, training and development, performance appraisal) and dependent variable (NHC's organisational performance). Findings reveal that recruitment and selection at NHC play a critical role on organisational performance. NHC has a well-communicated recruitment policy with high employee awareness and involvement. Internal advertising of new positions is common, supporting talent retention and growth opportunities. While job applicants generally receive adequate information, some inconsistencies exist. The recruitment process is perceived as transparent, though improvements are possible. NHC is seen as an equal-opportunity employer, but reinforcement is needed in some areas. Employees feel reasonably included in the selection process, which is merit-based with clearly defined criteria. Selected candidates generally fit well with the organisational culture. However, inclusiveness in selection could be enhanced. Recommendations include improving recruitment policy communication, increasing employee engagement, strengthening internal advertising, reinforcing equal opportunity practices, maintaining merit-based selection, ensuring cultural fit, and fostering trust and transparency. These suggestions aim to optimize recruitment and selection practices, thereby enhancing NHC's organizational performance and potentially benefiting other organizations.