Significant Individual Factors Research Articles

Patient Falls in Seclusion Rooms in Psychiatric Inpatient Care: A Sociotechnical Probabilistic Risk Modeling Study.

Patient falls are a major adverse event in psychiatric inpatient care. To model the risk for patient falls in seclusion rooms in psychiatric inpatient care. Sociotechnical probabilistic risk assessment (ST-PRA) was used to model the risk for falls. Data sources were the research team, literature review, and exploration groups of psychiatric nurses. Data were analyzed with fault tree analysis. The risk for a patient fall in a seclusion room was 1.8%. Critical paths included diagnosis of a psychiatric disorder, the mechanism of falls, failure to assess and prevent falls, and psychological or physical reason. The most significant individual risk factor for falls was diagnosis of schizophrenia. Falls that occur in seclusion events are associated with physical and psychological risk factors. Therefore, risk assessment methods and fall prevention interventions considering patient behavioral disturbance and physiological risk factors in seclusion are warranted.

Four in ten married women demands satisfied by modern contraceptives in high fertility sub-Saharan Africa countries: a multilevel analysis of demographic and health surveys

BackgroundDemand satisfied with modern contraceptive can be seen on both a health and economic level. Additionally, family planning helps to regulate fertility, prevent unintended pregnancies and their consequences. Thus, the aim of this study was to identify the magnitude of demand satisfied with modern contraceptive among married/in-union women in ten high fertility sub Saharan African countries.MethodsRecent Demographic and Health Surveys that included a weighted sample of 43,745 women of reproductive age provided the data for this study. All statistical analyses were conducted once the data had been weighted, and Stata version 16.0 was used. A multilevel mixed-effect binary logistic regression model was fitted. To determine statistically significant individual and community-level factors associated with demand satisfied for modern contraceptive, odds ratios with a 95% confidence interval was generated. A p-value less than 0.05 was declared as statistical significance.ResultsOverall, demand satisfied to use modern contraceptive in high fertility sub-Saharan Africa countries was 39.53% (95%CI: 39.06, 39.98). Women aged 25–34 (AOR: 1.34, 95%CI: 1.26, 1.42) and 35–49 (AOR: 1.28, 95%CI: 1.20, 1.38), women education: primary (AOR: 1.35, 95%CI: 1.27, 1.44) and secondary (AOR: 2.05, 95%CI: 1.90, 2.21), husband education: primary (AOR: 1.26, 95%CI: 1.18, 1.35) and secondary (AOR: 1.54, 95%CI: 1.43, 1.66), husband residence (AOR: 1.75, 95%CI: 1.60, 1.91), media exposure (AOR: 1.22, 95%CI: 1.15, 1.29), wealth index: poorer (AOR: 1.1, 95%CI: 1.02, 1.19), middle (AOR: 1.18, 95%CI: 1.08, 1.28), richer (AOR: 1.37, 95%CI: 1.26, 1.49) and richest (AOR: 1.34, 95%CI: 1.56, 1.89), number of children: 4–6 (AOR: 0.48, 95%CI: 0.43, 0.55) and above 6 (AOR: 0.39, 95%CI: 0.29, 0.59), perceived distance to the health facility not big problem (AOR: 1.11, 95%CI: 1.04, 1.15), urban residence (AOR: 1.18, 95%CI: 1.10, 1.27), high community level poverty (AOR: 0.85, 95%CI: 0.74, 0.97) were significantly associated with demand satisfied for modern contraceptives.ConclusionOnly four in ten married reproductive age women demands satisfied with modern contraceptives in high fertility Sub Saharan African countries. Modern contraceptives should therefore be more widely available, especially in rural areas and for those living away from health facilities. Also, increasing media exposure and education, providing financial support, and making contraceptive access easier for married women from poor households are important interventions that need to be put in place.

Machine learning models for predicting risk of depression in Korean college students: Identifying family and individual factors.

Depression is one of the most prevalent mental illnesses among college students worldwide. Using the family triad dataset, this study investigated machine learning (ML) models to predict the risk of depression in college students and identify important family and individual factors. This study predicted college students at risk of depression and identified significant family and individual factors in 171 family data (171 fathers, mothers, and college students). The prediction accuracy of three ML models, sparse logistic regression (SLR), support vector machine (SVM), and random forest (RF), was compared. The three ML models showed excellent prediction capabilities. The RF model showed the best performance. It revealed five significant factors responsible for depression: self-perceived mental health of college students, neuroticism, fearful-avoidant attachment, family cohesion, and mother's depression. Additionally, the logistic regression model identified five factors responsible for depression: the severity of cancer in the father, the severity of respiratory diseases in the mother, the self-perceived mental health of college students, conscientiousness, and neuroticism. These findings demonstrated the ability of ML models to accurately predict the risk of depression and identify family and individual factors related to depression among Korean college students. With recent developments and ML applications, our study can improve intelligent mental healthcare systems to detect early depressive symptoms and increase access to mental health services.

Risk factors for dexmedetomidine-associated bradycardia during spinal anesthesia: A retrospective study.

Sedation with dexmedetomidine is needed for patients undergoing spinal anesthesia. However, dexmedetomidine sedation increases the incidence of bradycardia. We aimed to identify and to evaluate risk factors for bradycardia in patients undergoing spinal anesthesia with dexmedetomidine sedation. The records of 91 patients who underwent spinal anesthesia with sedation using dexmedetomidine were reviewed retrospectively. For this study, we identified 15 characteristics of included patients from this group who underwent lower leg surgery and had an estimated blood loss of <300 mL. We collected details on potential risk factors for bradycardia from their clinical records. These factors included age, American Society of Anesthesiologists classification, height, weight, sensory level of spinal anesthesia, history of hypertension, diabetes mellitus, loading, and maintenance dose of dexmedetomidine, tourniquet time, initial diastolic and systolic blood pressure, initial heart rate (HR), and anesthesia and surgery duration. The primary endpoint of this study was the occurrence of bradycardia. We identified potential risk factors using logistic regression analysis. The incidence of bradycardia was obtained in 23 (25%) of 91 patients. Initial HR and tourniquet time were significant individual predictive factors for the occurrence of bradycardia. Logistic regression analysis showed that adjusted baseline HR and duration of tourniquet use were risk factors for bradycardia. Patients should be monitored when undergoing spinal anesthesia with sedation using dexmedetomidine for bradycardia when they have a long tourniquet time. A low initial HR could also be a predictive factor for bradycardia.

Multilevel analysis of undernutrition and associated factors among adolescent girls and young women in Ethiopia

BackgroundThe consequences of undernutrition have serious implication for the health and future reproductive periods of adolescent girls and young women aged 15–24 years. Inspite of this, they are neglected age groups and there is limited information about the nutritional status of this age group in Ethiopia. Therefore, estimating the extent and associated factors of undernutrition among adolescent girls and young women in a national context using multilevel analysis is essential.MethodsSecondary data analysis was conducted from the Ethiopian Demographic and Health Survey 2016. A total sample weight of 5362 adolescent girls and young women was included in this study. A multilevel mixed-effect binary logistic regression model with cluster-level random effects was fitted to determine the associated factors of undernutrition among adolescent girls and young women in Ethiopia. Finally, the odds ratios along with the 95% confidence interval was generated to determine the individual and community level factors of undernutrition. A p-value less than 0.05 was declared as the level of statistical significance.ResultsOverall, 25.6% (95%CI: 24.5–26.9) of adolescent girls and young women were undernourished. Statistically significant individual level factors includes adolescent girls and young women aged 15–19 years (AOR: 1.53, 95%CI: 1.32–1.77), individual media exposure (AOR: 0.82, 95%CI: 0.69–0.97), and unprotected drinking water source (AOR: 1.24, 95%CI: 1.04–1.48). Whereas, Southern Nations, Nationalities, and Peoples' Region (AOR: 0.33, 95%CI: 0.13–0.83) and rural residence (AOR: 1.69, 95%CI: 1.24–2.32), were community level factors for adolescent girls and young women undernutrition.ConclusionOne quarter of the Ethiopian adolescent girls and young women were undernourished. Therefore, the Ethiopian government should better engage this age group in different aspects of the food system. To improve nutritional status, public health interventions such as increased media exposure for rural residents and interventions that improve access to protected water sources will be critical.

Measuring and exploring mental health determinants: a closer look at co-residents’ effect using a multilevel structural equations model

ObjectivePrevious research has demonstrated that individual risk of mental illness is associated with individual, co-resident, and household risk factors. However, modelling the overall effect of these risk factors presents several methodological challenges. In this study we apply a multilevel structural equation model (MSEM) to address some of these challenges and the impact of the different determinants when measuring mental health risk.Study design and settingTwo thousand, one hundred forty-three individuals aged 16 and over from 888 households were analysed based on the Household Survey for England-2014 dataset. We applied MSEM to simultaneously measure and identify psychiatric morbidity determinants while accounting for the dependency among individuals within the same household and the measurement errors.ResultsYounger age, female gender, non-working status, headship of the household, having no close relationship with other people, having history of mental illness and obesity were all significant (p < 0.01) individual risk factors for psychiatric morbidity. A previous history of mental illness in the co-residents, living in a deprived household, and a lack of closeness in relationships among residents were also significant predictors. Model fit indices showed a very good model specification (CFI = 0.987, TLI = 0.980, RMSEA = 0.023, GFI = 0.992).ConclusionMeasuring and addressing mental health determinants should consider not only an individual’s characteristics but also the co-residents and the households in which they live.

Spatial Distribution and Associated Factors of Institutional Delivery among Reproductive-Age Women in Ethiopia: The Case of Ethiopia Demographic and Health Survey.

Background Maternal mortality is unacceptably high. About 295,000 women died during and following pregnancy and childbirth in 2017. The vast majority of these deaths (94%) occurred in low-resource settings, and most could have been prevented. Methods This research is based on a cross-sectional study using 2016 EDHS data. The analysis included 7,590 women who had given birth in the five years prior to the survey. Clusters with high and low hot spots with institutional delivery were found using SatScan spatial statistical analysis. A multilevel multivariable mixed-effect logistic regression was utilized to discover characteristics associated with institutional delivery. Result In this study, 33.25% of women who gave birth in the last 5 years preceding the survey delivered their babies at health institutions. The finding also indicated that the spatial distribution of institutional delivery was nonrandom in the country. Variables achieving statically significant association with utilization of institutional delivery were as follows: at the individual level, richness (AOR = 2.18, 95%CI: 1.39–3.41), higher education (AOR = 3.89, 95%CI: 1.51–10.01), a number of antenatal care visits of four and above (AOR = 6.57, 95%CI: 4.83–8.94), and parity of more than two children (AOR = 0.48, 95%CI: 0.34–0.68); at the community level, higher education (AOR = 1.70, 95%CI: 1.22–2.36) and urban residence (AOR = 5.30, 95%CI: 3.10–9.06) were variables that had achieved statically significant association for utilization of institutional delivery. Conclusions This study identified a spatial cluster of institutional delivery with the Somali and Afar region having low utilization rates and Addis Ababa and Tigray regions having the highest utilization rates. The significant individual factors associated with institution delivery were woman antenatal care visits, household wealth index, maternal education, and parity, and the significant community ones were region, place of residence, and educational status. Therefore, to maximize health facility delivery in Ethiopia, the predictors of institutional delivery identified in this study should be given more attention by governmental and nongovernmental stakeholders.

MODELLING THE EFFECT OF SELECTED FACTORS OF VIA-THE-TOP CONTENT (OTT) SERVICES IN KENYA

Purpose: The goal of this research was to develop a model for the impact of several factors on OTT adoption in Kenya.&#x0D; Materials and Methods: This study employed an explanatory research technique. The target population for this study was limited to 23076 subscribers, who were the active subscribers of Viusasa as of December 2018. Simple random sampling was used to sample form the target population. Data collection was done through a structured questionnaire, which was both in soft and hard copy. Statistical package for social sciences (SPSS v25.0) was utilized to analyze the responses received from the questionnaire. The study generated and used averages, frequencies, and percentages to present the analysis.&#x0D; Results: The findings revealed that individual factors (β=0.326, p=0.000), technological factors (β=0.268, p=0.000), organizational factors (users) (β=0.166, p=0.000) and environmental factors (users) (β=0.270, p=0.000) have a positive and significant relationship with the adoption of OTTs in Kenya. Pay TV providers and broadcasting stations that are providing or purpose to provide OTT services to focus on the key individual factors that lead to the adoption of OTT services. Based on the multiple regression findings, the null hypotheses were rejected since the P values were less than 0.05 and thus, there is a significant effect individual factors, technological factors, organizational factors and environmental factors on the adoption of OTT services in Kenya is not statistically significant.&#x0D; Unique contribution to theory, practice and policy: The study findings indicate that the theory of diffusion is applicable to the study of factors influencing the adoption of OTT. Pay TV providers and broadcasting stations that were providing or purposed to provide OTT services are encouraged to focus on the key individual factors that lead to the adoption of OTT services. The understandings assisted on the providers improving their relationship with the users and have a bigger market even in terms of the customer base. Telecom operators in Kenya are encouraged to develop competitive OTT services to gain a competitive advantage over the present OTTs’ players.

Time after time: detecting annual patterns in stream bacterial biofilm communities.

SummaryTo quantify the major environmental drivers of stream bacterial population dynamics, we modelled temporal differences in stream bacterial communities to quantify community shifts, including those relating to cyclical seasonal variation and more sporadic bloom events. We applied Illumina MiSeq 16S rRNA bacterial gene sequencing of 892 stream biofilm samples, collected monthly for 36‐months from six streams. The streams were located a maximum of 118 km apart and drained three different catchment types (forest, urban and rural land uses). We identified repeatable seasonal patterns among bacterial taxa, allowing their separation into three ecological groupings, those following linear, bloom/trough and repeated, seasonal trends. Various physicochemical parameters (light, water and air temperature, pH, dissolved oxygen, nutrients) were linked to temporal community changes. Our models indicate that bloom events and seasonal episodes modify biofilm bacterial populations, suggesting that distinct microbial taxa thrive during these events including non‐cyanobacterial community members. These models could aid in determining how temporal environmental changes affect community assembly and guide the selection of appropriate statistical models to capture future community responses to environmental change.

Multidrug-resistant organisms in paediatric palliative care patients - Prevalence, risk factors and the impact of a liberal hygiene concept.

Multidrug-resistant organisms (MDRO) deserve special attention in health-care facilities for children with life-limiting conditions because these children have an increased risk for colonisation. To avoid nosocomial transmissions to other inpatients, single-room isolation is usually recommended. In the context of paediatric palliative care (PPC), such isolation counters the aim of participation in social activities for the patients. This study aimed to determine the prevalence of MDRO, the predictive value of risk factors and the incidence of nosocomial infections and nosocomial colonisations on a PPC inpatient unit applying a special hygiene concept that enables participation in social activities through risk-adaption and barrier nursing. Two-year surveillance with MDRO screening of all intakes (N=386) of a PPC unit on the day of admission and discharge. To determine the predictive value of pre-defined risk factors, logistic regression analyses were calculated. Receiver operating characteristic analyses were performed to determine the predictive power of the number of risk factors on the presence of MDRO. The rate of MDRO colonisation at admission was 12.7%; previous positive MDRO screening was the only significant individual risk factor. Over the 2-year period, no MDRO-related nosocomial infections occurred; nosocomial colonisation incidence density was 0.6. Results demonstrate that patients with at least one risk factor have to be cared for by barrier nursing until MDRO screening results are negative. Following these guidelines prevents nosocomial MDRO transmission.

Individual, Social, and Environmental Correlates of Energy Drink Use Among Adolescents

ObjectiveTo investigate the correlates of adolescent energy drink (ED) use using a socioecological approach to inform future interventions. MethodsIn 2017–2018, 3,688 students attending 25 randomly selected Western Australian secondary schools completed a self-report survey. A backward stepwise logistic regression analysis was used to construct a model of the most significant individual (n = 12), social (n = 3), and environmental (n = 4) factors associated with being an ED user (ie, past month ED use). ResultsOverall, 18% of participants were ED users. The factors significantly associated with being an ED user included being male (P < 0.001), having a higher disposable income (P < 0.001), perceiving EDs as good for health (P = 0.009), perceiving EDs as safe for someone their age to drink (P < 0.001), having a sensation-seeking personality (P = 0.011), having friends who drink EDs (P < 0.001), having parents who would give them an ED if asked (P < 0.001), and having EDs available at home (P < 0.001). Conclusions and ImplicationsInitiatives to reduce ED intake among adolescents need to be multifaceted, addressing individual, social, and environmental factors. It is also important that educational interventions target both parents and adolescents.

Posture evaluation for the full-body dynamic simulation in JACK Siemens PLM

The objective of this study is to evaluate the accuracy of predicted postures in JACK Siemens software via comparing with the actual human movement recorded by the Xsens motion tracking system. Seventeen student volunteers were recruited in this study and required to perform a common lift task with different loads. Our statistical results revealed the significant difference of joint angular displacements (e.g., trunk, hips, elbows, knees, and shoulders) between the simulated human movements and actual measurements. Additionally, we estimated the spinal forces imposed on the lower back of participants, which were highly correlated to the joint poses. To provide robust ergonomics analysis, the manufacturers need to continue the efforts to improve their software by reconsidering the significant individual factors, such as the muscle strength between males and females, and the effect of loads on the adopted postures during task performance.

The Importance of Restricting Youth Access to Marijuana in the Age of Legalization: Lessons Learned From Tobacco Control Efforts and COVID-19 Restrictions

After a year of uncertainty regarding the effects of COVID-19 on youth substance use, the data are starting to come in and the picture is less dire than anticipated. Undoubtedly, young people with significant individual and family risk factors—such as high levels of emotional distress, anxiety, or financial strain—struggled this year both in terms of their mental health and substance use [1,2]. Still, use of the two most commonly used addictive substances by young people, e-cigarettes and alcohol, appears to have declined [3,4] among adolescents and college students during the course of stay-at-home orders in the U.S., even as the use of tobacco, alcohol, opioids, and other drugs increased among adults [5].

Family relationships and internet abuse in 25 European countries

The aim of this article is to understand the link between family relationships and internet abuse (IA) using a sample of 18,709 children in 25 European countries. Our results suggest that family relationships are a significant predictor of IA – even when controlling for other significant individual and country-level factors. According to our results, children in two-parent families were less likely to have IA than children in other types of homes, but their advantage seems to derive from having better family dynamics (manifest in more communicative and less autonomous lifestyles) rather than family structure as such. Moreover, the importance of family structure with respect to IA is mediated by children’s relational lifestyles. This suggests that positive parenting characterised by high levels of dialogue may work as a protective factor of IA. We also identified sociodemographic risk factors: IA is more common among older and male children, those with lower levels of self-efficacy, and those living in large cities. The specific components of advantageous relational lifestyles can guide interventions to protect children from IA.

Estimation of glucosamine in biomass of Trichoderma reesei cultivated on lignocellulosic substrates.

Effects of the compositions of lignocellulosic substrate including hemicellulose, cellulose, lignin, and protein on the glucosamine content in biomass of Trichoderma reesei TISTR3080 were studied. A synthetic solid surface media containing different ratios of xylan (hemicellulose), carboxymethyl cellulose (cellulose), lignin, and various concentrations of yeast extract (source of protein) were used to cultivated T. reesei. Regression analysis identified significant individual and interaction factors that affected glucosamine quantity in T. reesei biomass. A regression model was developed to estimate the glucosamine content in biomass of T. reesei from the compositions of the lignocellulosic substrate. An acceptable error (not more than 10%) of the regression model was obtained from validation with the experimental results of glucosamine content in biomass of T. reesei cultivated on lignocellulosic solid surface media made from copra waste and banana peel.

Children with School Absenteeism: Comparing Risk Factors Individually and in Domains

ABSTRACT This study investigated risk factors for school absenteeism in a sample of 983 children in elementary and lower secondary schools in Denmark, using administrative data on absenteeism measured in the year following risk factor measurement. Risk factors were measured by survey (children and teachers) and register data. Two methods of determining importance of risk factors were compared: individual risk factors versus four domains of risk factors (psychological problems, physical problems, school factors, and demographic and family factors). Significant individual risk factors were found in all four domains. When teacher-reports of the children’s psychological problems were used, psychological problems was the risk factor domain that predicted school absenteeism best and the school-related factors domain predicted worst. The results highlight the need to distinguish between single risk factors that identify groups of individuals with elevated risk of school absence and the detection of risk factor domains that better predict school absence.

Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

Early detection of Type 2 diabetes is necessary for its prevention. The prediction models for detection systems normally employ common factors that may not properly fit all persons having different health conditions. Therefore, this study proposes a method for type 2 diabetes prediction with factors representing personal health conditions. More specifically, this study proposes a novel prediction method named Average Weighted Objective Distance (AWOD) based on the assumption that the individual has diverse health conditions resulting from different individual factors, a requirement for an effective prediction model. AWOD is a modification of Weighted Objective Distance (WOD) by applying information gain to reveal significant and insignificant individual factors having different priorities, which are represented by different weights. For AWOD, the data set is divided into a training set used to determine all relevant thresholds and constant values required for AWOD calculation and the testing set. In particular, AWOD is designed for binary classification problems with a relatively small dataset. To validate the proposed method, two datasets from open sources, Pima Indians Diabetes (Dataset 1) and Mendeley Data for Diabetes (Dataset 2) each containing 392 records, were studied. The prediction performance for both datasets is compared with the machine learning-based prediction methods, including K-Nearest Neighbors, Support Vector Machines, Random Forest, and Deep Learning. The comparison results showed that the proposed method provided 93.22% and 98.95% accuracy for Dataset 1 and Dataset 2, respectively, which are higher than those provided by other machine learning-based methods.

Factors associated with physician consultation and medication use in children and adolescents with chronic pain: A scoping review and original data.

Although paediatric chronic pain is common, it is not yet clear which individuals with chronic pain are likely to seek health care for their pain. The aims of this study were to summarize the current evidence of the characteristics of children and adolescents with chronic pain who consult a physician or use medication for their pain. Additionally, we aimed to expand knowledge by further investigating key and promising, factors in a large community sample of adolescents. First, using scoping review methodology, studies on health care utilisation in paediatric chronic pain were identified by the systematic literature search. Out of 1,158 articles, 12 were included for data extraction. Second, in a population-based cross-sectional survey, data of N=2,280 adolescents (10-18years) and their parents (N=1,464), were analysed. Univariable logistic and multivariable LASSO regression models were calculated for adolescents with chronic pain (n=749) to identify predictors of physician visits or analgesics intake due to pain, controlling for acute illness and injury. The scoping review identified higher pain intensity, frequency, severity and pain-related disability as significant individual factors associated with physician consultation. Female sex and higher pain intensity were associated with medication consumption. Multivariable analyses with cross-sectional data revealed only pain-related school absence to be associated with physician consultation; analgesic medication use was associated with school absence and higher pain intensity. Original data from this study support prior findings. School absence and pain intensity, plausible surrogates for pain severity, are the most relevant factors in health care utilisation due to chronic pain. A scoping summary of existing research on predictors for physician consultation and medication use in children and adolescents with chronic pain is provided. Furthermore, the role of potential predictors for health care utilisation-sociodemographic, pain, school, psychological and parent-related characteristics-is analysed in a population-based cross-sectional survey. Controlling for illness and injuries, and using a strict chronic pain definition, the cross-sectional survey allows conclusions on healthcare utilisation specific to chronic pain in adolescents.

Weighted objective distance for the classification of elderly people with hypertension

Hypertension is a significant risk factor for cardiovascular disease (CVD) development and is commonly observed among elderly individuals. To prevent CVD complications, identifying groups according to their abilities to control hypertension is highly important. In previous studies, objective distance was proposed as a measurement for identifying groups of elderly people with hypertension. However, this measurement still has a problem requiring the further elimination of insignificant factors affecting classification accuracy. Therefore, a weighted objective distance (WOD) is proposed as a new measurement, which can address significant individual factors to obtain higher classification accuracy. WOD is based on the concept that hypertension controllability in each individual can be represented by different significant individual factors, which can be used for group classification. Therefore, WOD represents a modification of the original objective distance measurement, using weighting factors. Weighting factors obtained from information gain can prioritize factors according to their associated weights. WOD is assumed to be able to provide greater classification accuracy than the original objective distance. To test this assumption, the secondary data for 1198 elderly people with hypertension were collected. The WOD method provided classification results with 97.33% accuracy, which is higher than the original objective distance method, which provided 70.03% accuracy, consistent with the proposed assumption. To validate the WOD method, the classification accuracy was compared against those provided by decision tree and neural network classifiers. The results showed that the WOD method was more accurate than the decision tree and neural network classifiers, which provided 71.12% and 71.62% accuracy, respectively.

Individual and Contextual Factors Associated With School Connectedness in the Context of Counseling in Schools

School connectedness refers to students' beliefs that their peers and adults at school care about their learning and themselves as individuals. School connectedness has been widely documented as a significant predictor for positive student outcomes, yet little is known about factors associated with it as an outcome variable. Guided by the advocating student‐within‐environment framework situated with the social development model, this study explored individual and contextual factors associated with school connectedness among 1,201 students in Grades 4 to 8. Gender, ethnicity, grade level, and social skills were found to be significant individual factors, whereas school support for learning and acceptance of diversity were significant contextual factors. Together, the individual and contextual factors explained 47% of the variance in school connectedness. Our findings also revealed grade‐related variability concerning associations between the factors and school connectedness. These findings provide further insights into fostering connectedness in the context of counseling in schools with consideration to students' developmental needs.