Standard Linear Regression Research Articles

LBSUN215 Evaluation Of Glycemic Control By Continuous Glucose Monitoring Among Hospitalized Older Adults With Type-2 Diabetes And Frailty

Abstract Recent observational and meta-analyses have reported a frailty prevalence between 10% and 25% in people with diabetes, in particular in those older than 60 years of age. The impact of frailty on hospital glycemic control and glycemic variability (GV) by continuous glucose monitoring (CGM) in insulin-treated older adults with type 2 diabetes (T2D) is not known. Accordingly, we reviewed data from 3 inpatient randomized clinical trials using CGM in insulin-treated patients with T2D. The validated laboratory-based frailty index (FI-LAB) scale was used for frailty assessment, and participants were categorized into three groups [non-frail: (0-<0.1), pre-frail (≥0.1-<0.21), and frail (≥0.21)] in 84 older adults. | There were no differences on admission clinical characteristics between the non-frail/pre-frail older adults and the frail older adults except for Creatinine (BMI: 32.4 ± 9 vs. 36.83± 13, p=0.21; HbA1c%: 9.3 ± 2 vs. 8.72± 2, p=0.18; Admission BG: 227 ± 114 vs. 194. 07± 75 mg/dl, p=0.26; Cr: 1.16 ± 1 vs. 1.65± 1. 0). There were no differences in GV by coefficient of variation (CV), amplitude of glucose excursion (MAGE), and standard deviation (SD) between the two groups. The correlation between FI-LAB score and percent time with CGM <70 was 0.204 (p=0. 064) and the correlation between FI-LAB score and percent time with percent time with CGM<54 was 0.217 (p=0. 049). Results from standard linear regression and zero-inflated Beta regression further suggest that frail old patients with higher frailty scores may be associated with larger percent time with CGM below range <70 and CGM <54 mg/dL. Conclusion Our results indicate that older adults with T2D with higher frailty score experience more time in hypoglycemia during their hospital stay despite having comparable mean daily blood glucose, time in range and glycemic variability compared to non-frail or pre-frail older adults. A larger prospective study is needed to confirm these findings and determine the impact of frailty on clinical outcome. Providers should be vigilant when using insulin or insulin secretagogues in hospitalized older adults with diabetes and frailty. Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Correlates and predictors of clinical leadership need analysis (CLeeNA) for nurses: A cross-sectional web-based study.

Clinical leadership is the process of improving different organizational and patient care processes that achieve high-quality and safety of care outcomes. Clinical leadership is about having the appropriate skills and attributes to manage clinical settings. Nurses need to develop managerial and leadership skills; thus, nurses clinical leadership skills should be developed in all clinical settings. The clinical leadership needs of nurses received limited focus; the concept is still not fully understood. This study aimed to assess the correlates and predictors of nurses' clinical leadership needs in Jordan. Using a cross-sectional research design utilizing a purposive sample of 349 nurses were recruited from different hospitals. Pearson correlation coefficients standard multiple linear regressions were mainly used to analyze the data. The "leadership and clinical practice" was the highest mean score of the subscales, while the "financial and service management" was the lowest. At an alpha of 0.05, significant positive moderate correlations were found between nurses' clinical leadership needs and: level of education, age, years of experience in nursing, years of experience in leadership, number of employees under the direct supervision of the leader, and type of the hospitals. At an alpha of 0.01, significant positive weak correlations were found between nurses' clinical leadership needs and: marital status, title, and unit/ward organizational structure. Marital status-separated/divorced/widowed, and years of nursing-4 years or less predicted negatively affect nurses' clinical leadership needs. Meeting the clinical leadership needs of nurses will positively contribute to various clinical settings outcomes. Leadership training is needed to enhance nurses' clinical leadership skills and competencies.

The posterior capsule is distended in dysplastic hips, but the anterior capsule is not.

In the setting of acetabular dysplasia, the increased translational motion of the femur may damage the labrum and cartilage, as well as stretch the capsule. The purpose of the study was to investigate the relationship between the acetabular coverage and the capsular stiffness by assessing the distension of anterior and posterior joint recesses on the hip computed tomography arthrography. One hundred thirty-three patients (138 hips) with a median age of 36years (range 18-50years) who received the computed tomography arthrography for evaluation of nonarthritic hip pain in our institute between 2015 and 2017 were retrospectively reviewed. The maximal distance between the anterior/posterior capsule and the anterior femoral head-neck junction/posterior femoral head on the axial imaging of computed tomography arthrography was defined as the width of anterior/posterior joint recess. The width of anterior/posterior joint recess was adjusted with the diameter of the femoral head and was then compared between acetabular dysplasia (lateral center-edge angle < 25°), normal acetabulum (lateral center-edge angle between 25 and 39°), and deep acetabulum (lateral center-edge angle > 39°). In addition, the standard univariate linear regression analysis was used to investigate the relationship between the adjusted width of anterior/posterior joint recess and anterior/posterior coverage of the hip, determined by the anterior/posterior wall index. The adjusted width of posterior joint recess was significantly greater in the acetabular dysplasia group than the normal acetabulum and deep acetabulum groups (p < 0.01 and p = 0.02, respectively). There was no significant difference of the adjusted width of anterior joint recess between the groups (n.s.). The adjusted width of posterior joint recess had a significant but weak negative correlation with the anterior wall index (r = - 0.25, p < 0.001), and no correlation with the posterior wall index (r = - 0.0004, n.s.). There was no significant correlation between the adjusted width of anterior joint recess and the anterior/posterior wall index (r = 0.05, n.s./r = 0.07, n.s.). The distension of posterior capsule on the computed tomography arthrography was significantly greater in acetabular dysplasia. In addition, there was a significant but weak negative correlation between the distension of posterior capsule and the anterior coverage of the hip. It indicated a looser posterior capsule was observed in a dysplastic hip. The relevance of posterior capsular laxity to clinical outcomes warrants further investigation. Given the fact that the distension of anterior capsule was not significantly higher in acetabular dysplasia, the need of anterior capsular plication in a dysplastic hip should be carefully evaluated. Level III.

ENGLISH WRITING INSTRUCTION AND STRATEGIES AS PREDICTORS OF WRITING ANXIETY OF STUDENTS

This study aimed to determine English teachers’ writing instruction and students’ writing strategies significantly predict students’ ESL writing anxiety. The working group for the present study, in which quantitative descriptive-predictive design was employed, included 271 first-year students enrolled in GE 2 subject at UM Digos College. The participants answered adopted standard questionnaires to quantify which dimension/s of English Teachers’ writing instruction and English students’ writing strategies significantly predict/s writing anxiety. The gathered data were analyzed and interpreted using the mean and standard deviation, Pearson-r, regression, and multiple linear regression. Regression analysis was used to predict the value of writing anxiety based on the value of writing instruction and strategies. Multiple linear regression was used to model the linear relationship between writing anxiety and writing instruction and strategies. The results revealed that teachers’ explicit writing instruction in teaching and students’ employment strategies in writing helped the learners lessen their writing anxiety. The findings corroborated the three domains for English writing instructions and English writing strategies: learning to write factual texts, learning to write formal and informal texts, and resourcing, significantly predict English writing anxiety. The findings obtained in this study suggest that the employment of effective instruction and strategies for students may alleviate anxiety and desirably perform various writing tasks in any particular genre.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0908/a.php" alt="Hit counter" /&gt;&lt;/p&gt;

Financial Accountability and Financial Performance of Savings and Credit Cooperatives in Mid-Western Uganda

Purpose: To establish the association between financial accountability and Financial Performance of SACCOs in Mid-Western Uganda.&#x0D; Methodology: A cross-sectional research design and positivist paradigm were used to collect data from 93 SACCOs in Mid-Western Uganda using a close-ended questionnaire. Besides, a standard linear regression analysis was conducted. From the target population of 122 SACCOs, a sample of 93 SACCOs was selected using probability sampling technique.&#x0D; Findings: The study established a positive significant association between financial accountability and Financial Performance.&#x0D; Unique Contribution to Practice and Policy: The study confirms the theoretical assertions that financial accountability associates with financial performance. Besides, the study addresses the mixed findings on the link between financial accountability and financial performance. Also, the study suggests a need for management to consider financial accountability as a key driver of financial performance of SACCOs. Still, there is need for government to advocate for effective financial accountability to ensure that SACCOs attain sustainable financial performance.

Learning the Indicative Patterns of Simulated Force Changes in Soil Moisture by BP Neural Networks and Finding Differences with SMAP Observations

Soil moisture is a vital land surface variable that can influence climate change. Many problems in soil moisture data require the identification of signals obscured by anthropogenic external forces (including greenhouse gases such as CO2 and aerosol radiative force), natural forces (such as volcanic and solar activity), and internal variability (such as ENSO, NAO, and PDO). Although artificial neural networks (ANNs) have been widely studied in making accurate predictions, the studies of interpretation of ANNs in soil moisture are still rare. Hence, the proposed method aims to assist in the study of interpretating soil moisture data. Specifically, first, an ANN model is trained to predict the approximate year of the simulations by identifying the spatial patterns of qualitative changes in soil moisture. After accurately predicting the approximate year, the spatial patterns in the ANN model, acting as “reliable indicators” of the force changes, are the different natures of regional signals. Then, the simulated data and Soil Moisture Active and Passive (SMAP) observations are fed into the trained ANN separately, and the specific differences are observed by the Deep Taylor Decomposition (DTD) visualization tool. By comparing with the standard multiple linear regression method, the results of the ANN model can provide the reliable indicators of change for a specific year, thus providing meaningful information from the ANN model according to the common soil moisture data. The results show that a large correlation exists between eastern Asia and western North America during the 21st century, and the correlation increases with time in Australia. This also reflects the strong force signal due to a combination of anthropogenic and external forces that has played a role in soil moisture over the decades and can clearly discern the differences between model simulations and observed data. This study indicates that the proposed method using ANNs and visualization tools enables relatively accurate predictions and the discovery of unknown patterns within soil moisture data.

The Relationship between Body Composition and ECG Ventricular Activity in Young Adults.

This study aimed to determine the correlation between body composition (measured as weight, body mass index, and body fat percentage (BFP)) and electrocardiographic ventricular parameters (the QT and TQ intervals and the ratios between the electrical diastole and electrical systole (TQ/QT) and between the cardiac cycle and electrical diastole (RR/TQ), both for uncorrected and corrected intervals) in a sample of 50 healthy subjects (age interval 19–23 years, mean age 21.27 ± 1.41 years, 33 women and 17 men). Subjects’ measurements were performed with a bioimpedancemetry body composition analyzer and a portable ECG monitor with six leads. Starting from the correlations obtained between the investigated continuous variables, we performed a standard linear regression analysis between the body composition parameters and the ECG ones. Our results revealed that some of our regression models are statistically significant (p < 0.001). Thus, a specific part of the variability of the dependent variables (ECG ventricular activity parameters for corrected QT intervals) is explained by the independent variable BFP. Therefore, body composition influences ventricular electrical activity in young adults, which implies a differentiated interpretation of the electrocardiogram in these situations.

Generation of New Exciting Regressors for Consistent Online Estimation of Unknown Constant Parameters

The problem of estimating constant parameters from a standard vector linear regression equation in the absence of sufficient excitation in the regressor is addressed. The first step to solve the problem consists in transforming this equation into a set of scalar ones using the well-known dynamic regressor extension and mixing technique. Then, a novel procedure to generate new scalar exciting regressors is proposed. The superior performance of a classical gradient estimator using this new regressor, instead of the original one, is illustrated with comprehensive simulations.

Effects of Company Size, Profitability and Auditor's Reputation on Audit Delay And its Impact on Abnormal Return

The purpose of this research is to study the effect of firm size, profitability, auditor's reputation for delays in audit completion and its effect on abnormal returns in companies listed on the LQ45 Index. Secondary data for 2017-2019 from 25 companies used in this study were selected using a purposive sampling method according to research needs by selecting companies as samples. The technique of analyzing data in this study uses two regressions, namely multiple linear regression and standard linear regression which is processed using SPSS version 25. This study gives the results that company size (X1) and profitability (X2) partially have a negative and significant influence on delays. completion of the audit, while the reputation of the auditor (X3) in a partial way does not affect the delay in the completion of the audit (AD). Simultaneously, the three independent variables have a significant effect on the delay in audit completion. This study also provides results that the delay in audit completion has no effect on abnormal returns (Y) on LQ45 index companies in 2017-2019

Tropospheric Response to Stratospheric Variability via Lagged Quantile Regression

AbstractStratospheric variability may affect tropospheric weather on time scales between a few weeks and a few months, thus providing great potential for sub‐seasonal to seasonal weather forecasts of extremes. Unlike standard linear regression, which is based on the conditional mean of the response variable, lagged quantile regression is used here, which enables modeling the response variable's complete conditional distribution. Using the ERA5 reanalysis, we explore the relation of prominent teleconnection indices and gridded tropospheric fields to the stratosphere during boreal winter for various quantiles and lags. Significant differences in duration, strength, and direction of the stratosphere‐troposphere connection in outer quantiles compared to the median are identified. In particular, tropospheric extremes and their relation to the polar vortex strength are discussed, and an outlook is given.

Disentangling personalized treatment effects from "time-of-the-day" confounding in mobile health studies.

Ideally, a patient’s response to medication can be monitored by measuring changes in performance of some activity. In observational studies, however, any detected association between treatment (“on-medication” vs “off-medication”) and the outcome (performance in the activity) might be due to confounders. In particular, causal inferences at the personalized level are especially vulnerable to confounding effects that arise in a cyclic fashion. For quick acting medications, effects can be confounded by circadian rhythms and daily routines. Using the time-of-the-day as a surrogate for these confounders and the performance measurements as captured on a smartphone, we propose a personalized statistical approach to disentangle putative treatment and “time-of-the-day” effects, that leverages conditional independence relations spanned by causal graphical models involving the treatment, time-of-the-day, and outcome variables. Our approach is based on conditional independence tests implemented via standard and temporal linear regression models. Using synthetic data, we investigate when and how residual autocorrelation can affect the standard tests, and how time series modeling (namely, ARIMA and robust regression via HAC covariance matrix estimators) can remedy these issues. In particular, our simulations illustrate that when patients perform their activities in a paired fashion, positive autocorrelation can lead to conservative results for the standard regression approach (i.e., lead to deflated true positive detection), whereas negative autocorrelation can lead to anticonservative behavior (i.e., lead to inflated false positive detection). The adoption of time series methods, on the other hand, leads to well controlled type I error rates. We illustrate the application of our methodology with data from a Parkinson’s disease mobile health study.

Exploring what motivates the development of upcycling practices.

Faced with an aggressive production model with the use of resources, initiatives such as upcycling arise that seek to extend the life of products. This paper analyzes the role of individuals’ principles and values as driving factors, something that has not been studied in depth to date. The study is based on a field work in which 830 subjects from 180 countries have participated in a survey, from September 2019 to September 2020. This large sample of participants at the international level is one of the key contributions of the work, since it allows us to contrast conclusions from the few previous works and issue new ones based on the sequence of analysis described below, which broadens the empirical base in this field of study. The data so collected was analysed using the statistical software Stata and estimating standard multiple linear regressions, ordered logit and logit regressions. The findings confirmed the chain of actions - doing, frequency, entrepreneurship - is decisively conditioned by personal values and principles. This sequence of analysis constitutes the main contribution of our work. With regards to entrepreneurship, this work provides evidence that having the intention to start up an upcycling business is more likely when individuals consider upcycling important for them and report themselves as having a high probability of upcycling; but it is less likely among women and the older age groups (more than 55 years old).

Calibration and Validation of CYGNSS Reflectivity through Wetlands’ and Deserts’ Dielectric Permittivity

The reflection of Global Navigation Satellite Systems (GNSS) signals, namely GNSS-Reflectometry (GNSS-R), has recently proven to be able to monitor land surface properties in the microwave spectrum, at a global scale, and with very low revisiting time. Moreover, this new technique has numerous additional advantages, including low cost, low power consumption, lightweight and small payloads, and near real-time massive data availability, as compared to conventional monostatic microwave remote sensing. However, the GNSS-R surface reflectivity values estimated through the bistatic radar equation, and the Fresnel coefficients have shown a lack of coincidence with real surface reflectivity data, mostly due to calibration issues. Previous studies have attempted to avoid this matter with direct regression methods between uncalibrated GNSS-R reflectivity data and external soil moisture content (SMC) products. However, calibration of GNSS-R reflectivity used in traditional inversion models is still a challenge, such as those to estimate SMC, freeze/thaw, or biomass. In this paper, a successful procedure for GNSS-R reflectivity calibration is established using data from the CYGNSS (Cyclone GNSS) constellation. The scale and bias parameters are estimated from the theoretical dielectric properties of water and dry sand, which are well-known and empirically validated values. We employ four calibration areas that provide maximum range limits of reflectivity, such as deserts and wetlands. The CYGNSS scale factor and the bias parameter resulted in a = 3.77 and b = 0.018, respectively. The derived scale and bias parameters are applied to the CYGNSS dataset, and the retrieved SMC values through the Fresnel reflection coefficients are in excellent agreement with the Soil Moisture Active Passive (SMAP) SMC product. Then, the SMAP SMC is used as a reference true value, and provides a standard linear regression with an R-square coefficient of 0.803, a root mean square error (RMSE) of 0.084, and a Pearson’s correlation coefficient of 0.896.

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

Increased penetration of grid-connected PV systems in modern electricity networks induces uncertainty factors to be considered from several different viewpoints, including the system’s protection and management. Accurate short-term prediction of a grid-connected PV park’s output is essential for optimal grid control and grid resilience. Out of the numerous types of models employed to this end during the last decade, artificial neural networks, (ANNs) have proven capable of handling the uncertainty issues of solar radiation. Insolation and ambient, or panel temperature, are most commonly employed as the independent variables, and the system’s output power is successfully predicted within 3 to 5% error. In this paper, we apply a common type of ANN for the long-term prediction of a 100 kWp grid-connected PV park’s output, by exploiting experimental data from the last 8 years of operation. Solar radiation and backsheet temperature were utilized for the ANN training stage. The performance metrics of this model, along with a standard linear regression model, are compared against the actual performance data. The capabilities of the ANN model are exploited in the effort to decouple the fluctuating effect of PV panel soiling which interferes with the efficiency degradation process. The proposed methodology aimed to quantify degradation effects and is additionally employed as a fault diagnosis tool in long-term analysis.

Statistical learning to estimate energy savings from retrofitting in the Norwegian food retail market

Buildings worldwide consume about 40% of all produced energy and are major contributors to GHG emissions. Hence, to reach the 2030 European energy efficiency target it is vital to reduce the energy consumption in buildings. An important barrier that hinders renovation projects is uncertainty regarding the expected savings. The main objective of this paper is to present two different statistical methods to estimate energy savings. The two methods are easy to implement for practitioners within the energy retrofitting industry, and at the same time has acceptable precision and reliability. The two methods are applied at 5 different food retail stores that undertook renovation in 2019. The models are trained on data from 2018 (one whole year before any of the retrofitting's took place) and are further applied to estimate the energy savings in 2021. The first method is the Tao Vanilla benchmarking method (TVB). The TVB model predict energy consumption in buildings on an hourly level. The model has received a lot of attention within the load forecasting literature and has previously proved its performance in machine learning competitions. The TVB has a straightforward specification, and the model parameters are easily understood. This is the first study that apply the TVB to estimate energy savings in a large retrofitting project within the energy and building sector. The second method relies on a more common industrial approach, which is to use weekly data and energy temperature curves to document energy savings. In addition, we demonstrate a novel approach of using broken line (BL) models to estimate energy savings. The suggested BL approach can simultaneously estimate all the model parameters and yield a full covariance matrix within a standard linear regression framework. The results from the retrofitting projects demonstrates considerable energy savings between 25% and 55%. Furthermore, both the TVB and the BL models deliver reliable precision. The estimated energy savings from both models are coinciding. This indicates that they could jointly be used to gain insight that may lead to more informed decisions for energy saving projects. The TVB model proves to be a proficient benchmarking model that can give detailed hourly information about the savings. The BL model is used to gain intrinsic details about the buildings varying cooling and heating needs depending on the outside temperature during the year.

A catchment-scale model of river water quality by Machine Learning

Water quality is a concern in most river basins worldwide due to the widespread release of pollutants which impacts the freshwater ecosystems. Exploring the relationships between drivers and water quality parameters at the regional scale is key in the identification of appropriate actions for the reduction of water pollution. Regional models are the appropriate tool to achieve this, though their development poses relevant challenges because of the complexity and non-linearity of such relationships. Among the available approaches, Machine Learning (ML) is promising because of its capability to detect complex nonlinear relationships and flexibility in the parameterization, which is learned from data. In this work, we developed regional models of water temperature, dissolved oxygen, arsenic, sulfate and chloride concentrations, as well as electrical conductivity, by using two ML algorithms, Random Forest and Deep feed-forward Neural Network, and compared their performances against the standard Linear Regression model. Our results indicate that the two ML algorithms are much more accurate models for such variables than the classical Linear Regression model, with Deep feed-forward Neural Network being the most effective in identifying the reciprocal importance of the drivers and capturing nonlinear relationships between drivers and water quality variables. Our analysis also revealed that the Julian day and year at which the sample was taken surrogate the air temperature in modeling water temperature and dissolved oxygen, with only a slight performance reduction. Arsenic, sulfate, and chloride show more complex behaviors in which geogenic and anthropogenic sources are intertwined. Dilution exerts a role chiefly for arsenic concentration, which suggests a non-uniform, in space, geogenic origin for this variable.

Determinant factors of mathematics achievement in online learning during the covid-19 pandemic

Online learning has become more popular during the Covid-19 pandemic, including in mathematics. However, many educators have raised concerns whether there are critical factors determining the mathematics achievement during online learning. This study examines online learning readiness (OLR), math anxiety (MA), online platforms used in learning (Zoom Meeting and Google Classroom) and gender as determinants of mathematics achievement students in online learning. The participants in this study were 197 students of a private university in Samarinda, Indonesia. Online learning readiness and math anxiety were gathered by means of questionnaires. The questionnaires were deemed valid ( 0.500) and reliable (OLR = 0.903; MA = 0.888) based on Confirmatory Factor Analysis (CFA) and Cronbach’s Alpha respectively. Other variables function as dummy based on the attributes possessed and learning experienced by the participants. The data were analyzed using standard multiple linear regression and multiple linear regression with dummy variables. The results reveal that online learning readiness, math anxiety, online learning platforms and gender had a significant effect on students' mathematics achievement in online learning. In addition, online learning platforms and gender contribute to the difference effect of online learning readiness and math anxiety to mathematics achievement. The results suggest that educators and educational institutions should consider these important aspects to optimize the advantages and effectiveness of online learning.

The Impact of Take-home Open-book Examinations due to COVID-19 among Business Students. Do Gender, Age, and Academic Skills Matter?

Due to COVID-19, numerous universities and colleges have been forced to arrange home-based exams in many countries. We know relatively little about what consequences this might have for the ranking of students based on qualifications in the various subjects. This is an important issue for administrators, educators, and others involved in planning the design of higher education. The intention of this article is to get more insight into this issue. By analysing administrative data from a Norwegian Business School, we examined the impact of moving from traditional school exams to home-based exams in 2020 due to COVID-19. The chosen methodology is the comparison of means by using t-test and standard linear regression models. The results indicate a weaker link between high school performance and achievements in business administration courses. Furthermore, home-based exams might disadvantage older students. This is useful knowledge in the judgement as to whether or not to introduce home-based exams as a permanent arrangement.

THE EFFECTS OF L2 PRAGMATIC AUTONOMOUS AND CONTROLLED MOTIVATIONS ON ENGAGEMENT WITH PRAGMATIC ASPECT

No study has investigated the relationship between student engagement per se and student motivation within second language (L2) pragmatics, notwithstanding the significance of engagement for L2 learning. The present study aimed to explore the effects of two global motivational orientations (autonomous and controlled motivations) on behavioral engagement within the perspective of L2 pragmatics by drawing on self-determination theory. A total of 76 college students agreed to participate and were requested to fill out a tailor-made, 34-item, 6-point Likert-scale questionnaire. The results of data analysis using standard multiple linear regression revealed that both Autonomous and Controlled Motivations significantly predicted and explained a large amount of variance in Engagement, F(2, 71) = 161.28, p &lt; .01, R2 = .82, adjusted R2 = .81, and that the effect of Controlled Motivation, B = .33, t(71) = 8.05, p &lt; .01, was twice as large as that of Autonomous Motivation, B = .16, t(71) = 4.91, p &lt; .01. These findings indicate that students’ controlled motivation is more powerful in enhancing their engagement in learning L2 pragmatics. Pedagogically, it implies that teachers should bolster students’ motivation to learn L2 pragmatics, which can eventually lead to their increased engagement.

ORGANIZATIONAL MINDFULNESS OF SCHOOL HEADS AND WORK ENGAGEMENT OF PUBLIC SCHOOL TEACHERS

The purpose of this study is to determine which domain of organizational mindfulness of school heads significantly influences the work engagement of teachers. To do so, the study employed the non-experimental quantitative research design employing the descriptive-correlational technique, and N=134 randomly selected teachers from different elementary schools of Matanao II District in Davao del Sur, Philippines were the research participants. Weighted mean and standard deviation, Pearson r and multiple linear regression analysis are the statistical tools employed in analyzing and interpreting the data. Analysis reveals that both organizational mindfulness of school heads and work engagement of teachers were of a high level. The study was able to establish the significance of the relationship between organizational mindfulness of school heads and the work engagement of teachers. Lastly, despite both mindful behavior of school heads that best influence work engagement of teachers.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0931/a.php" alt="Hit counter" /&gt;&lt;/p&gt;