Chi-squared Automatic Interaction Detection Analysis Research Articles

Chi-square automatic interaction detection (CHAID) analysis of the use of safety goggles and face masks as personal protective equipment (PPE) to protect against occupational biohazards

BackgroundThis study represents the first Spanish investigation to rigorously evaluate compliance with the use of safety goggles and face masks as essential personal protective equipment (PPE) in companies with workplaces involving exposure to biological agents. ObjectivesThis study aimed to examine the degree of use of face masks and safety goggles as personal protective equipment (PPE), the factors that influence their use, and the profile of workers exposed to occupational biological agents in Spanish companies in the health sector, farming sector, meat industry, waste treatment plants, food industry, and veterinary centers. MethodsWe conducted a cross-sectional descriptive study involving 590 Spanish workers from 51 companies. We developed a 34-item questionnaire to assess workers’ perception of risk related to exposure to biological agents in their workplaces. Among the questions, three were designed to measure the degree of use of key protective equipment in sectors with biological agent exposure: protective gloves, safety goggles or face masks. We only analyzed safety goggles and face masks. We performed various statistical analyses, including Cronbach’s alpha, frequency of endorsement, content validity ratio using Lawshe’s method, varimax rotation, the Kaiser-Meyer-Olkin test, and Bartlett’s sphericity test, to assess the internal consistency and reliability of the questionnaire. Additionally, we employed a chi-square automatic interaction detection (CHAID) segmentation analysis, using workers’ responses regarding their attitudes toward safety goggles and face mask usage as PPE for protection against biological risks, with demographic variables as independent factors. ResultsIn the current study, CHAID analysis revealed that workers exposed to group 2 biological agents used more safety goggles and face shields compared with workers exposed to other groups of biological agents. Moreover, workers in laboratories and the food industry used face masks more than workers of other sectors. ConclusionThe CHAID analysis in the current study indicated that workers exposed to biological agents from both group 2 and group 3 demonstrated satisfactory levels of compliance and utilization of protective masks, surpassing their counterparts in terms of usage. Workers in the food and laboratory industries had subpar compliance with preventive measures, and employees from companies with internal health and safety departments exhibited significant adherence to workplace mask usage, safeguarding themselves against biological risks.

Determination of Factors Affecting International Students’ Satisfaction Levels Using CHAID Analysis

Internationalization has recently been a core element for higher education institutions (HEIs) worldwide. Various components provide internationalization in HEIs. However, international students stand out from the others due to providing numerous benefits to host countries. Therefore, HEIs around the world seek to increase the number of international students and their satisfaction levels. This study, inspired from the importance of international students, seeks to determine the factors affecting the satisfaction levels of international students who study at a Turkish state university. To this end, an on-line survey consisting of 25 questions were administered to a total of 330 international students studying at Middle East Technical University (METU). Survey data were evaluated using the CHAID (Chi-Squared Automatic Interaction Detection) analysis. The findings of the analysis indicated that the international students’ implications, study degree and gender played a major role in their satisfaction levels. Considering these influential factors determined by the CHAID analysis will contribute to the internationalization strategies which is developed to attract more international students to HEIs.

Main Predictor(s) of Good Corporate Governance in National Government Departments: A Decision-tree Classification and Prediction Analyses

Purpose: Good public sector corporate governance leads to good management, stewardship of public wealth, public engagement, and ultimately, better outcomes for citizens. As South Africa has one of the worst Gini coefficients in the world, its public sector should effectively address the challenges of inequality, poverty, and unemployment. Yet, almost 30% of national and provincial government departments obtain unfavourable audit outcomes. Methodology: Supporting the drive for clean audit outcomes that equate to good corporate governance, the research objective was to determine the predictors of national government departments’ corporate governance success. The study used CHAID (chi-squared automatic interaction detection) analyses, a decision-tree technique based on information reported over a 13-year period. Findings: The CHAID analyses indicated that national government departments’ corporate governance success is primarily explained by three factors, namely: a quality internal audit function (main predictor), the number of fraud incidents, and the number of internal control weaknesses. Implication: Even though all the variables in this study receive attention in literature and regulation, the problem of unfavourable audit outcomes persists. The question is where to focus on resolving the problem. Regulators may take note of where to focus their efforts in strengthening elements of good corporate governance.

Educating the digital generation: the role of virtual communities

Introduction. Modern educational space, as well as socialisation, exists in the framework of two interrelated components: the real (material) and virtual (digital) world. Adolescent self-realisation takes place in this mixed, or hybrid, space employing its means. One of them is virtual communities, i.e. communities existing on the platforms of digital resources, using digital devices, information and communication technologies. According to forecasts related to the near future, interaction in communities through ones digital counterparts and artificial intelligence will become a prerequisite for successful performance. However, teachers, having no experience of growing up in the conditions of the unfolding digital world, cannot always objectively assess the role of its factors and apply them as means of education. Aim. The present research aimed to investigate the influence of virtual communities on adolescent self-realization, to determine the conditions that guarantee positive influence and, thus, to validate and justify the pedagogical guidelines that will foster constructive interaction in virtual communities. Methodology and research methods. The study was based on the cultural-historical theory by L. S. Vygotsky. According to it, virtual communities are considered as a way of self-realization for adolescents in the context of digital society culture. Through virtual communities an adolescent undergoes socialization in a mixed environment in the course of his/her own activities and interaction with representatives of certain cultures. The constructive approach sets a course to identify the principles of teaching staff support for the subsequent teenagers’ successful usage of this tool. Data collection was carried out in the form of an online survey using the Yandex Forms Internet service in 2022. 2996 school students of the Chelyabinsk region aged from 13 to 17 years took part in the survey. The authors employed the methods of mathematical data processing: Somers’ D-test, Kramer’s V-test, CHAID (Chi Squared Automatic Interaction Detection) analysis. Results. The majority of teenagers (87 %) are involved in various types of virtual communities (educational, cognitive, and entertaining). The participation in virtual communities has a direct relation to involvement in different types of activities and respective satisfaction with self-realization. It generally implies being involved in several types of virtual communities, spending moderate amount of time in virtual communities, participating in virtual learning societies. Virtual communities have undeniable educational potential. The realization of this potential ensures constructive interaction and self-realization of adolescents, and requires pedagogical support. Scientific novelty. It is proved that virtual communities influence adolescent self-realization. The article determines the conditions of the constructive virtual community influence on adolescent self-realization. The research also substantiates the pedagogical principles, which guarantee the educational potential fulfillment (polysubjectivity, productive online-offline interaction, and proactivity). Moreover, the article established that the strategy of digital educational transformation should set a course to the transition from ensuring accessibility and security in a virtual environment to managing virtual communities for the purpose of the self-development and self-realization. Practical significance. The research outcomes may be applied for the development of educational programmes and the implementation of educational activity in the realities of a digital world.

Application of Multivariate Logistic Regression and Decision Trees to Assess Factors Influencing Prevalence of Abortion and Stillbirth in Yankasa sheep

AbstractThis study was carried out to assess factors influencing prevalence of abortion and stillbirth in Yankasa sheep using multivariate logistic regression and decision trees. 191 kidding records of Ewes from a total of 50 traditional Yankasa sheep herders within Nasarawa South agro-ecological zone from the year 2020-2021 were utilized in the study. Sampling was restricted to only farmers that were able to give information on Lamb, Ram and Ewe identification as well as occurrence of abortion, stillbirth (defined as a kid born dead or dying within 24 hours after birth), kidding date or period, parity and number of foetuses. Three seasons of abortion or stillbirth were generated according to the month of the year: rainy season (from May to October), dry season (from February to April) and harmattan season (from November to January). The logit of the probability of an abortion or stillbirth was modelled using logistic regression assuming an asymptotic binomial distribution. The Chi-square goodness of fit test was performed to check if the multivariate logistic model fitted the data well (P>0.05). Chi-square automatic interaction detection (CHAID) algorithm was also employed to model incidence of abortion or stillbirth. The chi-square analysis revealed that parity did not significantly (P>0.05) affect the incidence of abortion. However, season of the year significantly (P<0.05) affected prevalence of abortion, which was higher in the harmattan (38.5%) compared to the rainy (11.5%) and dry season (16.5%), respectively. Parity and season of the year did not affect significantly (P>0.05) the prevalence of stillbirth. The binary logistic regression analysis showed that season affected significantly (P<0.05) the prevalence of abortion, especially in the harmattan period with a high odds ratio (4.539). This was also confirmed by the result of the CHAID analysis. The present information could be exploited in management practices to reduce to the incidence of abortion in order to improve the production level of the sheep farmers.

Using entrepreneurial competencies and action to profile entrepreneurs: a CHAID analysis approach

PurposeEntrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial competencies (ECs) can predict entrepreneurial action (EA) for distinct profiles, such as male versus female, start-up versus established and for entrepreneurs within different age groups and educational levels.Design/methodology/approachThe research was conducted using a survey method on a large sample of 1,150 South African entrepreneurs. Chi-squared automatic interaction detection (CHAID) algorithms were used to build decision trees to illustrate distinct entrepreneurial profiles.FindingsEach profile has a different set of ECs that predict EA, with a growth mindset being the most significant predictor of action. Therefore, this study confirms that a “one-size-fits-all” approach cannot be applied when profiling entrepreneurs.Research limitations/implicationsFrom a pedagogical standpoint, different combinations of these ECs for each profile provide priority information for identification of appropriate candidates (e.g. the highest potential for success) and training initiatives, effective pedagogies and programme design (e.g. which individual ECs should be trained and how should they be trained).Originality/valuePrevious work has mostly focused on demographic variables and included a single sample to profile entrepreneurs. This study maintains much wider applicability in terms of examining profiles in a systematic way. The large sample size supports quantitative analysis of the comparisons between different entrepreneurial profiles using unconventional analyses. Furthermore, as far as can be determined, this represents the first CHAID conducted in a developing country context, especially South Africa, focusing on individual ECs predicting EA.

Bullying Involvement at the Intersection of Gender Identity/Modality, Sexual Identity, Race, Ethnicity, and Disability: Prevalence Disparities and the Role of School-Related Developmental Assets.

Purpose: This study examines adolescents' self-reported school-based developmental assets and four intersecting social positions as they relate to prevalence of bullying involvement. Methods: Participants were 80,456 ninth and 11th grade students who participated in the 2019 Minnesota Student Survey (30.2% youth of color; 11% lesbian/gay/bisexual/pansexual/queer/questioning; 2.9% transgender/gender diverse [TGD] or gender questioning). Exhaustive Chi-square Automatic Interaction Detection analysis was used to identify school-based developmental assets (i.e., school safety, school adult support) and intersecting social positions (i.e., sexual identity; gender identity/modality; racial/ethnic identity; physical disabilities/chronic illness; and/or mental health/behavioral/emotional problems) associated with the highest prevalence of involvement as physical and relational bullies, victims, and bully-victims. Results: Adolescents with 2+ marginalized social positions who often lacked school-based developmental assets were part of nearly all the highest prevalence bullying involvement groups. TGD and gender questioning adolescents, Native American youth, and youth living with both physical disabilities/chronic illness and mental health/emotional/behavioral problems-most of whom had additional marginalized social positions and lacked school-based assets-were particularly overrepresented in high prevalence groups. For example, 31.1% of TGD or gender questioning youth of color living with both types of disabilities/health problems who did not feel strongly that school was safe reported involvement as physical bully-victims-nearly six times the sample average rate. Conclusion: Adolescents with multiple marginalized social positions and those lacking certain school-based assets-often overlapping categories-were involved in bullying at higher-than-average rates. Findings underscore the need for schools to address intersecting experiences of stigma and structural oppression that may perpetuate bullying involvement disparities.

Evaluating the Utility of Prostate-Specific Antigen Density in Risk Stratification of PI-RADS 3 Peripheral Zone Lesions on Non-Contrast-Enhanced Prostate MRI: An Exploratory Single-Institution Study.

Objective This study aimed to explore the potential of prostate-specific antigen density (PSAD) as a supplementary tool for defining high-risk Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions in the peripheral zone on non-contrast-enhanced MRI. This additional stratification tool could supplement the decision-making process for biopsy, potentially helping in identifying higher-risk patients more accurately, minimizing unnecessary procedures in lower-risk patients, and limiting the need for dynamic contrast-enhanced (DCE) scans. Materials and methods Between January 2019 and April 2023, 30 patients with PI-RADS 3 lesions underwent MRI-ultrasound fusion biopsies at our institution. Age and PSAD values were investigated using logistic regression and chi-square automatic interaction detection (CHAID) analysis to discern their predictive value for malignancy. Results The mean patient age was 64.7 years, and the mean PSAD was 0.13 ng/mL2. Logistic regression demonstrated PSAD to be a significant predictor of cancer (p=0.012), but not age (p=0.855). CHAID analysis further identified a PSAD cut-off value of 0.12, below which the cancer detection rate was 23.1% and above which the rate increased to 76.5%. Conclusions This exploratory study suggests that PSAD might be utilized to enhance the stratification of high-risk PI-RADS 3 lesions in the peripheral zone on non-contrast-enhanced MRI, aiding in decision-making for biopsy. While biopsy remains the gold standard for definitive diagnosis, a high PSAD value may suggest a greater need for biopsy in this specific group. Although further validation in larger cohorts is required, our findings contribute to the ongoing discourse on optimizing PI-RADS 3 lesion management. Limitations include a small sample size, the retrospective nature of the study, and the single-center setting, which may impact the generalizability of our results.

Exploring Bias-Based Bullying and Intersecting Social Positions as Correlates of Sexual Risk Behaviors Among Adolescents.

Purpose: The current study extends the limited body of intersectional research on adolescents' sexual health by examining experiences of bias-based bullying and multiple intersecting social positions associated with engagement in sexual risk behaviors. Methods: Participants were 14,968 sexually active 9th and 11th grade students surveyed as part of the 2019 Minnesota Student Survey (15% lesbian/gay/bisexual/queer/pansexual/questioning [LGBQ] and/or transgender/gender diverse [TGD] or gender questioning). Exhaustive Chi-square Automatic Interaction Detection analysis was used to identify experiences (i.e., bias-based bullying victimization) and intersecting social positions (i.e., sexual orientation identity; gender identity/modality; race/ethnicity; physical disabilities/chronic illness; mental health/behavioral/emotional problems) associated with the highest prevalence of three sexual risk behaviors. Results: Overall, 18% of adolescents reported 3+ sex partners in the last year, 14% reported drug/alcohol use before last sex, and 36% reported not discussing protection from sexually transmitted infections with new sexual partners. Adolescents with 2+ marginalized social positions, some of whom also experienced bias-based bullying, were part of 53% of the highest prevalence risk groups. For example, 42% of Multiracial or Latina/x/o gender questioning adolescents who identified as LGBQ reported 3+ sex partners in the last year-twice the sample average. Adolescents who were Black, American Indian/Alaska Native, Latina/x/o, Multiracial, TGD, or gender questioning were in the highest prevalence nodes across all outcomes. Conclusion: Adolescents with multiple marginalized social positions and who experience bias-based bullying engage in high-risk sexual behaviors at higher-than-average rates. Findings underscore the importance of addressing intersecting experiences of stigma to reduce high-risk sex behaviors and promote health equity among adolescents.

Interpersonal protective factors for LGBTQ+ youth at multiple intersecting social identities and positions.

Interpersonal supports are protective against multiple negative health outcomes for youth such as emotional distress and substance use. However, finding interpersonal support may be difficult for youth exposed to intersecting racism, heterosexism, and cisgenderism, who may feel they are "outsiders within" their multiple communities. This study explores disparities in interpersonal supports for youth at different sociodemographic intersections. The 2019 Minnesota Student Survey includes data from 80,456 high school students, including measures of four interpersonal supports: feeling cared about by parents, other adult relatives, friends, and community adults. Exhaustive Chi-square Automatic Interaction Detection analysis was used to examine all interactions among four social identities/positions (racialized/ethnic identity, sexual identity, gender identity, sex assigned at birth) to identify groups who report different rates of caring from each source (Bonferroni adjusted p<.05). In the overall sample, 69.24% perceived the highest level of caring ("very much") from parents, 50.09% from other adult relatives, 39.94% from friends, and 15.03% from community adults. Models identified considerable differences in each source of support. For example, more than 72% of straight, cisgender youth reported their parents cared about them very much, but youth who identified as LGBQ and TGD or gender-questioning were much less likely to report high parent caring (less than 36%) across multiple racialized/ethnic identities and regardless of sex assigned at birth. Findings highlight the importance of better understanding the ways interpersonal support might differ across groups, and underscore a need for intersectionality-tailored interventions to develop protective interpersonal supports for LGBTQ+ youth, rather than one-size-fits-all approaches.

Predicting early versus late recovery from sport-related concussion using decision tree analysis.

Accurately predicting early (≤ 14 days) versus typical (15-27 days) or delayed (≥ 28 days) recovery from sport-related concussion (SRC) may allow for improved resource utilization and precision in planning and carrying out rehabilitation. In this study, the authors sought to develop an algorithm that enables accurate differentiation of recovery periods and duration after SRC. The authors hypothesized that data regarding initial symptom burden as quantified by a Post-Concussion Symptom Scale (PCSS) score, time to presentation, and number of prior concussions would be the most useful for analyzing predictive factors for concussion recovery duration. A retrospective case-control study was conducted to assess the primary outcome of days to clinical recovery following SRC in pediatric patients. Data from patients 12-18 years old presenting within 28 days of injury to an SRC clinic between November 11, 2017, and October 10, 2020, were analyzed. Patients with positive evidence of injury on head imaging or incomplete records were excluded. The primary outcome was duration of clinical recovery, grouped as early (≤ 14 days), typical (15-27 days), or delayed (≥ 28 days). Recovery was defined as follows: 1) symptom resolution or return to baseline, or 2) initiation of graduated return to play. CHAID (chi-square automatic interaction detection) analysis was used to optimize a decision tree based on 16 input factors, including age, sex, initial PCSS score, time to clinic presentation, number of prior concussions, and presence of defined symptom clusters. The cohort was randomized into training (70%) and test (30%) samples for algorithm validation. A total of 493 patients met the inclusion criteria (mean age 15.7 ± 1.5 years, 68.2% male, 70.0% White). The median time to presentation was 5 days (IQR 2-10 days). Most patients (52.3%) recovered within 14 days of injury, 21.5% recovered within 15-27 days, and 26.2% had a recovery period of 28 days or longer. The variables most predictive of recovery were initial PCSS score (cutoffs ≤ 6, 7-28, or ≥ 29), time to presentation (≤ 7 vs > 7 days), or prior concussions (0 vs ≥ 1). The model accurately discriminated between early versus typical or delayed recovery duration groupings (area under the curve 0.80, Youden index 0.44), and correctly classified > 90% of patients who recovered early. This novel three-factor predictive tool enabled accurate discrimination of early versus typical or delayed SRC recovery to better allocate resources, counsel patients, and make timely referrals.

Oral care frequency of patients with intensive care needs after surgery: A cross-sectional study

Although oral care for critical patients when applied effectively, minimizes important health risks, no study has been found that determines the oral care frequentness after surgery. This study was conducted to determine the oral care frequentness requirements and related factors of surgical patients admitted to the postoperative intensive care unit. It was a cross-sectional study. It was conducted with 110 surgical patients who were hospitalized in the postoperative intensive care unit and whose oral care was not yet performed within the first 6 hours. “Sociodemographic and Clinical Characteristics Form” and “Intensive Care Oral Care Frequency Assessment Scale” were used to collect data. Descriptive statistics and Chi-square automatic interaction detection analysis were applied in the analysis of the data. The mean score of the Intensive Care Oral Care Frequency Assessment Scale of the patients after surgery was 21.60±3.02. The oral care needs frequency score of 23.6% of the patients was between 10-19 points, they were identified as needing oral care should be at least 8 to 12 hours, and oral care needs of 76.4% were between 20-29 points and they were identified as needing oral care should be at least 6 hours. It was determined that the oral care frequentness score of the patients over 65 was higher than the other age groups and it was statistically significant that they needed oral care should be at least 6 hours (p&lt;0.001). In addition, it was determined that the oral care frequentness increased in patients aged 65 and over, who had been intubated, and had undergone general surgery, cardiovascular surgery, and urological surgery (p=0.008). This study showed that surgical patients who need intensive care after surgery had increased oral care frequentness care needs and had certain surgical risk factors that negatively affect their oral health.

Media-generated characteristics of Homeland War–related commemorations in Croatia

This study contributes to literature concerning memory and dark tourism by providing empirical evidence about the link between commemorations and the news media in a post-conflict and tourism-dependent Croatia, as well as the specific media-constructed social reality. The main focus lies on the types and media-based descriptions of commemorations. A total of 371 news articles were collected and then reviewed using a set of items grounded in theory. In addition to content, a chi-square automatic interaction detection analysis was also employed. Four commemoration types with significant peculiarities (site category, young and senior generation of visitors, and mass visits) are observed in a two-branch model. Other general characteristics of commemorations were also identified. The dominant commemoration type in Croatia are ‘memorial services’, (massively) visited by seniors. Young people rarely attend commemorations. If so, they mostly attend ‘parades, marches, processions’ taking place at authentic sites. The findings provide theoretical and practical implications for stakeholders.

Determinants of compulsory hospitalisation at admission and in the course of inpatient treatment in people with mental disorders—a retrospective analysis of health records of the four psychiatric hospitals of the city of Cologne

BackgroundWe aimed to identify differences in predictors of involuntary psychiatric hospitalisation depending on whether the inpatient stay was involuntary right from the beginning since admission or changed from voluntary to involuntary in the course of in-patient treatment.MethodsWe conducted an analysis of 1,773 mental health records of all cases treated under the Mental Health Act in the city of Cologne in the year 2011. 79.4% cases were admitted involuntarily and 20.6% were initially admitted on their own will and were detained later during the course of in-patient stay. We compared the clinical, sociodemographic, socioeconomic and environmental socioeconomic data (ESED) of the two groups. Finally, we employed two different machine learning decision-tree algorithms, Chi-squared Automatic Interaction Detection (CHAID) and Random Forest.ResultsMost of the investigated variables did not differ and those with significant differences showed consistently low effect sizes. In the CHAID analysis, the first node split was determined by the hospital the patient was treated at. The diagnosis of a psychotic disorder, an affective disorder, age, and previous outpatient treatment as well as the purchasing power per 100 inhabitants in the living area of the patients also played a role in the model. In the Random Forest, age and the treating hospital had the highest impact on the accuracy and decrease in Gini of the model. However, both models achieved a poor balanced accuracy. Overall, the decision-tree analyses did not yield a solid, causally interpretable prediction model.ConclusionCases with detention at admission and cases with detention in the course of in-patient treatment were largely similar in respect to the investigated variables. Our findings give no indication for possible differential preventive measures against coercion for the two subgroups. There is no need or rationale to differentiate the two subgroups in future studies.

A simple algorithm to predict disability in community-dwelling older Japanese adults

BackgroundWith a worldwide aging population, the prevention of disability in older adults has become an important issue. Therefore, the purpose of this study was to develop a model for predicting disability risk in older adults based on multiple factors, using a decision tree analysis. This model may be used with a mobile application when it is difficult to interview older adults, and to obtain individualized information for prioritizing interventions. MethodsWe examined the data from a cohort study conducted by the National Center for Geriatrics and Gerontology-Study of Geriatric Syndromes. We included 12,000 older adults without a disability and performed a decision tree analysis using the Chi-square automatic interaction detection (CHAID) algorithm. ResultsAmong the 12,000 participants without a disability, 11,503 and 497 participants remained disability-free and developed disability, respectively. The CHAID analysis identified 24 end nodes with five levels of partition and 16 partitioning variables for 34 questionnaire variables, with incident disability probabilities ranging from 0.0% to 96.7%. The classification accuracy and area under the curve of the CHAID model were 73.4% and 0.76, respectively. We found that maintaining mental health was important for older adults in their 80s and older, and that lifestyles and geriatric syndromes were important factors for those in their 70s. ConclusionsThe magnitude of the influences on the risk of developing a disability differ by age group. The results of this study may provide useful information for the development of mobile applications that predict the risk of developing disability and create tailor-made interventions.

Resilience: Key Factors Associated With Resilience of Older People in Botswana

This study aims to determine key factors that predict resilience in older people. A cross-sectional design and quantitative methods were used for this study. Four districts were selected in Botswana using cluster random sampling. Data on resilience from 378 older adults aged 60 years+ [Mean Age ( SD) = 71.1(9.0)] was collected using snowballing technique. Data on socio-demographics, protective and risk factors were also collected from urban and rural areas. CHAID (Chi-squared Automatic Interaction Detection) analysis was used to predict the strengths of the relationships among resilience and all predictor variables because the data were skewed. Five major predictor variables reached significance to be included in the model: depression, QOL, social impairment, education, and whether participants paid for services or accessed free services, along with high self-esteem ( p &lt; .001), security, and self-efficacy ( p &lt; .05). The presence of depression symptoms (χ2 = 23.7, p = .001, df = 1) and self-esteem (χ2 = 39.6, p &lt; .001) had the greatest influence on resilience. Older people with no depression symptoms but had low QOL still had social impairment (χ2 = 3.9, p &lt; .05). Older people with no depression symptoms had moderate to high QOL but had low resilience as a result of paying for services (χ2 = 7.4, p &lt; .02). Both protective and risk factors had a significant influence on resilience. Knowledge about the predictors of resilience in older people may assist stakeholders devise effective intervention, especially now with COVID-19 ravaging the country. Additionally, policies and programs inclined to assist older people may be established and implemented.

IDENTIFIKASI FAKTOR-FAKTOR YANG MEMENGARUHI PRESTASI MAHASISWA PROGRAM SARJANA DI INSTITUT PERTANIAN BOGOR MENGGUNAKAN METODE CHAID

IPB University (IPB) is one of the best universities in Indonesia, based on the Ministry of Education and Culture (Kemendikbud) clustering in 2020. As the best university, IPB requires efforts to improve the quality of its education. One of these efforts is to improve student achievement. This study aims to identify the factors that influence the competition and non-competition achievements of undergraduate students at IPB. The data used are achievement data (academic year 2016/2017 to 2020/2021) from the Directorate of Student Affairs and Career Development (Ditmawa) of IPB and demographic data of undergraduate level IPB students (entry year 2016/2017 to 2019/2020) from the Directorate of Administration and Education (Dit-Ap) IPB. The analytical method used in this study is the Chi-square Automatic Interaction Detection (CHAID) classification method. There was an imbalance of data on the Student Achievement response variable. Therefore, in this study, unbalanced data handling was also carried out by resampling in the form of oversampling, undersampling, and over-undersampling methods. The results showed that the classification using CHAID analysis with resampling in the form of oversampling with a balance accuracy of 73.7% resulted in the best classification performance. The factors that influence student achievement are 11 variables, and the 3 most influential variables are variables of year of admission, department, and last GPA.

Substance Use Behaviors Among LGBTQ+ Youth of Color: Identification of the Populations Bearing the Greatest Burden in Three Large Samples

PurposeResearch has identified persistent disparities in alcohol, e-cigarette, and marijuana use, by sexual orientation, gender identity, and race/ethnicity. Using an intersectionality framework, the present study analyzes three large datasets to identify intersecting social positions bearing the highest burden of substance use. MethodsData from adolescents in grades 9–12 in three samples (2019 Minnesota Student Survey, 2017–2019 California Healthy Kids Survey, and 2017 National Teen Survey) were harmonized for an analysis (N = 602,470). A Chi-squared Automatic Interaction Detection analysis compared the prevalence of four types of substance use across all combinations of four social positions (six racial/ethnic identities, five sexual orientations, three gender identities, and two sexes assigned at birth). For each substance, 10 intersectional groups with the highest prevalence of use were examined. ResultsIn the full sample, 12%–14% of participants reported past 30-day alcohol, e-cigarette, or marijuana use and 7% reported past 30-day binge drinking. Several intersecting marginalized social positions were consistently found to bear a high burden of substance use. For example, transgender and gender diverse (TGD) Latina/x/o young people, particularly those assigned male at birth, were in the high prevalence groups for alcohol use, binge drinking, and marijuana use. Black TGD or gender-questioning youth were commonly in the high prevalence groups. DiscussionFindings suggest that support, resources, and structural changes specifically tailored to youth with multiple marginalized identities (especially TGD) may be needed. The results argue for intersectional efforts that explicitly address racial/ethnic and cultural differences, while also integrating awareness and understanding of sexual and gender diversity.

Non-compliance with a nurse\u2019s advice to visit the primary care provider: an exploratory secondary analysis of the TRIAGE-trial

BackgroundDuring the cluster randomised TRIAGE-trial, a nurse advised 13% of low-risk patients presenting at an emergency department in Belgium to visit the adjacent general practitioner cooperative. Patients had the right to refuse this advice. This exploratory study examines the characteristics of refusers by uncovering the determinants of non-compliance and its impact on costs, as charged on the patient’s invoice.MethodsBivariate analyses with logistic regressions and T-tests were used to test the differences in patient characteristics, patient status, timing characteristics, and costs between refusers and non-refusers. A chi-square automatic interaction detection analysis was used to find the predictors of non-compliance.Results23.50% of the patients refused the advice to visit the general practitioner cooperative. This proportion was mainly influenced by the nurse on duty (non-compliance rates per nurse ranging from 2.9% to 52.8%) and the patients’ socio-economic status (receiving increased reimbursement versus not OR 1.37, 95%CI: 0.96 to 1.95). Additionally, non-compliance was associated (at the 0.10 significance level) with being male, not living nearby and certain reasons for encounter. Fewer patients refused when the nurse perceived crowding level as quiet relative to normal, and more patients refused during the evening. The mean cost was significantly higher for patients who refused, which was a result of more extensive examination and higher out-of-pocket expenses at the ED.ConclusionsThe nurse providing the advice to visit the general practitioner cooperative has a central role in the likelihood of patients’ refusal. Interventions to reduce non-compliance should aim at improving nurse-patient communication. Special attention may be required when managing patients with a lower socio-economic status. The overall mean cost was higher for refusers, illustrating the importance of compliance.Trial registrationThe trial was registered on registration number NCT03793972 on 04/01/2019.

Developing a Clinical Prediction Rule for Gait Independence at Discharge in Patients with Stroke: A Decision-Tree Algorithm Analysis

ObjectivesTo develop a clinical prediction rule (CPR) for gait independence at discharge in patients with stroke, using the decision-tree algorithm and to investigate the usefulness of CPR at admission to the rehabilitation ward. Materials and MethodsWe included 181 subjects with stroke during the postacute phase. The Chi-squared automatic interaction detection analysis method with 10-fold cross-validation was used to develop two CPRs; CPR 1 using easily obtainable data available at admission; CPR 2 using easily obtainable data available 1 month after admission, for prediction of gait independence at discharge. ResultsThe degree of independence of toileting was extracted as a first node in the development of two CPRs to predict gait independence at discharge. CPR 1 included the presence of delirium. CPR 2 included problem-solving abilities. The accuracy and area under the curve of CPR 1 were 84.5% and 0.911, respectively; those of CPR 2 were 89.0% and 0.958, respectively. ConclusionsToileting independence is a key factor in predicting the gait independence for the discharge of patients with stroke during the postacute phase. Early intervention, during the acute phase, for delirium and cognitive decline, as well as for toileting, increases the possibility of gait independence at discharge.