scholarly journals Medication Lists and Brown Bag Reviews: Potential Positive and Negative Impacts on Patients Beliefs about Their Medicine

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Cornelia Jäger ◽  
Jost Steinhaeuser ◽  
Tobias Freund ◽  
Joachim Szecsenyi ◽  
Katja Goetz
Keyword(s):  
Regression Models ◽  
Negative Impact ◽  
Quality Standards ◽  
Linear Regression Models ◽  
Medication List ◽  
Sociodemographic Data ◽  
Negative Impacts ◽  
Multimorbid Patients ◽  
With Memory ◽  
Medication Counselling

Introduction. Medication lists and structured medication counselling (SMC) including “brown bag reviews” (BBR) are important instruments for medication safety. The aim of this study was to explore whether patients’ use of a medication list is associated with their beliefs about their medicine and their memory of SMC. Methods. Baseline data of 344 patients enrolled into the “Polypharmacy in Multimorbid Patients study” were analysed. Linear regression models were calculated for the “specific necessity subscale” (SNS) and the “specific concerns subscale” (SCS) of the German “Beliefs About Medicine Questionnaire,” including self-developed variables assessing patients’ use of a medication list, their memory of SMC, and sociodemographic data. Results. 62.8% (n=216) remembered an appointment for SMC and 32.0% (n=110) BBR. The SNS correlated positively with regular receipt of a medication list (β=0.286, p<0.01) and negatively with memory of a BBR (β=-0.268; p<0.01). The SCS correlated positively with memory of a BBR (β=0.160, p=0.02) and negatively with the comprehensiveness of the mediation list (β=-0.224; p<0.01). Conclusions. A comprehensive medication list may reduce patients’ concerns and increase the perceived necessity of their medication. A potential negative impact of BBR on patients’ beliefs about their medicine should be considered and quality standards for SMC developed.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

scholarly journals Socioeconomic Classes of University Students in Pakistan as Predictors of their Personality Traits: A Case Study

2021 ◽  
Vol 2 (2) ◽  
pp. 205-221
Author(s):  
Rafaqat Ali ◽  
Furrukh Bashir ◽  
Rashid Ahmad
Keyword(s):  
University Students ◽  
Personality Traits ◽  
Regression Models ◽  
Negative Impact ◽  
Positive Impact ◽  
Socioeconomic Class ◽  
Lower Socioeconomic ◽  
Negative Impacts ◽  
The Impact

The current study was heading for determining the impact of Pakistani university students’ socioeconomic classes on their personality traits. Demographic and personality questionnaires were filled by available university students online. The stepwise regression technique facilitated to generate regression models to define impacts of different socioeconomic classes on students’ different personality traits. Different regression models highlighted the significant negative impacts of the middle upper socioeconomic class on Agreeableness, Extraversion and Neuroticism personality traits. The lower socioeconomic class was found to have positive impact on only one personality sub-trait self-discipline. Whereas, the upper lower socioeconomic class caused positive impacts on students’ trust sub-trait, Conscientiousness trait and negative impact on excitement seeking sub-trait of personality. The importance of these impacts of socioeconomic classes on different personality traits and the possible implications are discussed with respect to university students’ academic performance and academic behaviour.

It Must have been Burnout: Prevalence and Related Factors among Spanish PhD Students

2020 ◽  
Vol 23 ◽  
Author(s):  
Miguel A. Sorrel ◽  
José Ángel Martínez-Huertas ◽  
María Arconada
Keyword(s):  
Regression Models ◽  
Burnout Syndrome ◽  
Linear Regression Models ◽  
Related Factors ◽  
Psychological Variables ◽  
Design And Implementation ◽  
Dissertation Advisor ◽  
Sociodemographic Data ◽  
Common Response ◽  
Prolonged Stress

Abstract Recent studies in different countries indicate that PhD students are more vulnerable to psychological disorders compared to the general population. No such data are available for the Spanish population. This study addresses this issue by studying prevalence rates and factors related to a common response to prolonged stress such as burnout syndrome. Burnout, emotional abilities, resilience, satisfaction with the dissertation advisor, and sociodemographic data were collected from 305 PhD students. The results indicated that the burnout rates are high in this group, especially for the emotional exhaustion dimension. Different linear regression models explained between 14% and 41% of the overall burnout scores variance and its dimensions. The psychological variables and the satisfaction with the dissertation advisor were the most relevant predictors. Consistent with what has been found in other countries, the evidence found indicates that the mental state of PhD students in Spain is alarming. The results of this study have important implications for the design and implementation of interventions to alleviate this problem.

scholarly journals Lonely in the City–Sociodemographic Status and Somatic Morbidities as Predictors of Loneliness and Depression among Seniors–Preliminary Results

2021 ◽  
Vol 18 (14) ◽  
pp. 7213
Author(s):  
Kasper Sipowicz ◽  
Marlena Podlecka ◽  
Łukasz Mokros ◽  
Tadeusz Pietras
Keyword(s):  
Social Support ◽  
Older Adults ◽  
Primary Care ◽  
Risk Factor ◽  
Regression Models ◽  
Group Living ◽  
Living Alone ◽  
Linear Regression Models ◽  
Sociodemographic Data ◽  
The City

Up to a third of the population of older adults has been estimated to suffer from feelings of loneliness, which is considered a risk factor of depression. The aim of this paper is to compare the perceived level of loneliness and depression in seniors living in the country and in the cities and assess somatic morbidity and sociodemographic status as predictors of loneliness and depressiveness. n = 92 older adults in primary care units filled out a set of questionnaires: authors’ survey on sociodemographic data and morbidities, Beck Depression Inventory II (BDI, to measure depressiveness) and De Jong Gierveld Loneliness Scale (DJGLS, to assess loneliness). There was a strong, positive and statistically significant correlation between the BDI and DJGLS scores (R = 0.855, p < 0.001). City residents had on average higher BDI and DJGLS scores. Linear regression models were constructed to predict BDI and DJGLS scores. The set of statistically significant predictors were similar for BDI and DJGLS. Sociodemographic status and somatic morbidities accounted for around 90% of variance of depressiveness and loneliness scores in the studied group. Living alone was found to be the strongest relative predictor of both loneliness and depressiveness in the studied sample of the older adults. Our current results suggest that there might be a need to improve social support in the late adulthood as an intervention to diminish the sense of loneliness and depressiveness.

scholarly journals Factors Associated With the Outcome of a First-Line Intervention for Patients With Hip or Knee Osteoarthritis or Both: Data From the BOA Register

2020 ◽  
Vol 100 (10) ◽  
pp. 1771-1781
Author(s):  
Andrea Dell’Isola ◽  
Therese Jönsson ◽  
Håkan Nero ◽  
Frida Eek ◽  
Leif Dahlberg
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Regression Models ◽  
Negative Impact ◽  
Psychological Status ◽  
Linear Regression Models ◽  
First Line ◽  
Factors Associated ◽  
Independent Variables ◽  
Knee Oa

Abstract Objective This study explored the association of patients’ demographics, health status, symptom severity, previous osteoarthritis (OA) care, and psychological status with the change in pain severity following a first-line intervention including education and exercise for OA provided nationwide in Swedish primary care. Methods This register-based cohort study included 23,309 people with knee or hip OA from the Better Management of Patients with OA register. Linear regression models were used to assess the association of independent variables with the change in pain from baseline to 3 and 12 months. All the analyses were stratified based on the affected joint (hip vs knee). Results In people with hip and people with knee OA, high levels of baseline pain were associated with decreased pain at both follow-ups (3 months: knee B = −.67; hip B = −.64; 12 months: knee B = −.70; hip B = −.66), whereas being older, overweight, or female had a weak or no association. Finally, at both follow-ups, bilateral OA was associated with increased pain only in people with knee OA, whereas comorbidities and the willingness to undergo surgery were associated with increased pain regardless of the affected joint. Conclusions Baseline pain showed the strongest association among the analyzed variables, whereas sex, age, and body mass index appear to be weakly associated with the pain change after a first-line intervention. Comorbidities and willingness to undergo surgery showed a potentially important association and may have a negative impact on the pain change following a first-line intervention. Impact In people with hip or knee OA, age, sex, body mass index, and previous surgery are only weakly associated with the change in pain after a first-line intervention supporting the evidence recommending exercise and education as a foundation for all OA therapy. Having comorbidities and being willing to undergo surgery is associated with a worse outcome from a first-line intervention, including exercise and education. Individualized treatments addressing the disease perception and the specific comorbidity profile may improve the outcomes.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jaffer Okiring ◽  
Adrienne Epstein ◽  
Jane F. Namuganga ◽  
Victor Kamya ◽  
Asadu Sserwanga ◽  
...  
Keyword(s):  
Regression Models ◽  
Malaria Incidence ◽  
Temporal Changes ◽  
Linear Regression Models ◽  
High Burden ◽  
Exponential Regression ◽  
Malaria Morbidity ◽  
Laboratory Test Results ◽  
Combining Data ◽  
Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

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