Research on Farmers' Willingness to Participate in Rural E-commerce Activities and Influencing Factors Based on a Multivariate Logistic Model

Ecocompensation projects (EPs) have two primary objectives: environmental protection and the livelihood maintenance of farmers. Farmers’ ecocompensation expectations (FEEs) are a key factor that affects whether the design of ecological policy is targeted at practical problems. This article divides FEEs into three dependent variables and uses logistic regression and multiple regression models to analyze the influencing factors of FEEs. The results of a questionnaire survey based on 259 farmers in the area of Returning Farmland to Forest Project, with tropical and subtropical regions of China included, show that, first, farmers’ willingness to participate in EPs is strong. Several indicators, such as policy cognition level, returned farmland area, and participation in other EPs, have been demonstrated to significantly affect farmers’ willingness to participate. Second, the result of the contingent valuation method shows that farmers’ expectations of compensation income are higher than the current standard. Farmers’ returned farmland area, participation in other EPs, and degree of satisfaction with the policy effect are primary influencing factors. Third, farmers’ expectations of compensation mode, such as employment opportunities, technical guidance, and ecological migration, are greatly improving. The choice of compensation mode is mainly affected by policy cognition level, current compensation mode for returning farmland, and degree of satisfaction with the policy effect. This study can provide a new perspective for the policy adjustment of eco-environmental protection and farmers’ livelihood choices in the tropical and subtropical regions of China.

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Awareness and attitude of cancer patients about participation in clinical research (CR) in Saudi Arabia.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e17528 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e17528-e17528 ◽

Cited By ~ 1

Author(s):

Nagham Ramzi Sheblaq ◽

Sara Traifi ◽

Moahmmed Al Sakiani ◽

Ahmed Gasmelseed ◽

Mostafa A Abolfotouh ◽

...

Keyword(s):

Higher Education ◽

Cancer Patients ◽

Influencing Factors ◽

Standard Treatment ◽

Multivariate Analyses ◽

Public Awareness ◽

Cross Sectional Study ◽

Willingness To Participate ◽

Cross Sectional ◽

Institutional Review

e17528 Background: Enrollment of patients in CR may be impacted by their levels of awareness and attitude toward participation. Our study aims at assessing the awareness and willingness to participate in CR and identifying the barriers and influencing factors for participation. Methods: A cross-sectional study was performed using an interview-based questionnaire composed of close-ended questions conducted by trained research coordinators. Descriptive statistics, univariate and multivariate analyses were applied. Results: A total of 300 patients were enrolled in the study with a median age of 53.6 (42.2 - 64.0), and 188 patients (62.67%) were females. The majority of patients 289 (97.31%) were not aware of what is institutional review board. On the other hand, 226 (75.33%) showed interest in CR participation, if offered. The trust in treating physician (88.33 %) and the belief that participation in CR offers a better chance of cure (73.14%) were the two most important influencing factors to participate, while the preference of receiving standard treatment (43.13%) and the unknown efficacy of treatment (40.32%) were the top two barriers to participate. In univarate analyses, patients younger than 45 years (p<0.015) and those with higher education (more than middle school) (p<0.022) were significantly more interested in CR participation. However; in a multivariate analysis, higher education was the only significant predictor of interest to participate (p<0.023). Conclusions: There is an apparent gap in cancer patients’ awareness about CR. However, there is an obvious interest in participation in CR especially in patients with higher education level. Enhancing public awareness is crucial to improve CR participation.

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The Influencing Factors of Participation in Online Timebank Nursing for Community Elderly in Beijing, China

Frontiers in Public Health ◽

10.3389/fpubh.2021.650018 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yan Wu ◽

Yanran Ding ◽

Cong Hu ◽

Lei Wang

Keyword(s):

Influencing Factors ◽

Population Aging ◽

Elderly Care ◽

The Elderly ◽

Young Group ◽

Elderly Group ◽

Willingness To Participate ◽

Youth Group ◽

Beijing China ◽

Positive Effect

This study uses the logit model through questionnaire data of Beijing in 2019 to investigate the participation willingness of online timebank elderly care, especially to discover different influencing factors on the participation willingness between the youth group and the elderly group. We find that: First, the health status of elderly people and the number of elder families of young people have significant positive impacts on their willingness to participate in online timebank. Second, the experience of participating in voluntary activities has a significant positive effect and it has a far greater impact in the young group than that in the elderly group. Third, the more the free time, the higher the participation willingness in the young group, but it is the opposite in the elderly group. Fourth, the years of education and party member have significant promoting effects on the participation willingness in both groups. Such heterogeneous influencing factors can help develop online timebank nursing for dealing with the increasingly serious population aging problem in China and also other developing countries.

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A multivariate logistic regression model based on 3T mpMRI, clinical, and biopsy parameters for the prediction of prostate cancer extracapsular extension.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.6_suppl.27 ◽

2020 ◽

Vol 38 (6_suppl) ◽

pp. 27-27

Author(s):

Sohrab Afshari Mirak ◽

Steven Raman

Keyword(s):

Prostate Cancer ◽

Radical Prostatectomy ◽

Regression Model ◽

Predictive Value ◽

Logistic Model ◽

Extracapsular Extension ◽

Adc Value ◽

Model Based ◽

Multivariate Logistic Model ◽

Patient Size

27 Background: Extracapsular extension (ECE) of prostate cancer (PCa) is a poor prognostic factor associated with progression, recurrence after treatment and increased PCa- related mortality. Accurate staging prior to radical prostatectomy is crucial in avoidance of positive margins and when planning nerve-sparing procedures. This study investigated the predictive value of clinical, biopsy & 3TmpMRI parameters using a multivariate logistic model for per-lesion detection of PCa ECE with wholemount histopathology (WMHP) as reference. Methods: This IRB approved, HIPAA compliant study included 575 patients with 774 true positive PCa lesions, who underwent radical prostatectomy between 7/2010-2/2019. The relationship between pathologic ECE & parameters including clinical; age, prostate specific antigen (PSA) & PSA density (PSAD), biopsy; percentage of positive systematic cores & Gleason score (GS) & 3TmpMRI; prostate volume, number of lesions per patient, size, location, level, PIRADSv2 score, laterality, apparent diffusion coefficient (ADC) value & risk of ECE on MRI was evaluated using bivariate and multivariate analysis. The accuracy of the final model was evaluated using ROC analysis. Results: 27.8% (215/774), 42.9% (332/774) & 29.3% (227/774) of the lesions were PIRADSv2 score 3, 4 & 5 & 59.9% (464/774), 24.7% (191/774) & 17.7% (137/774) were low, intermediate & high risk for ECE, respectively. 23.6% (183/774) of the lesions had ECE on WMHP. On bivariate analysis higher PSA, PSAD, percentage of positive biopsy cores, biopsy GS, size, PIRADSv2 score, ADC value, risk of ECE on MRI, location (posterior), level(midgland & base), bilaterality & lower number of lesions per patient were significant for ECE prediction. The multivariate logistic model included age, PSAD, number of lesions per patient, size, location, level, PIRADSv2 score & risk of ECE on MRI. The AUC for the prediction of ECE for this model was 0.85. Conclusions: This multivariate regression model based on clinical, biopsy & 3TmpMRI parameters have a high predictive value for pathology ECE detection.

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Role of Glycated Hemoglobin in the Prediction of Future Risk of T2DM

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2010-1698 ◽

2011 ◽

Vol 96 (8) ◽

pp. 2596-2600 ◽

Cited By ~ 30

Author(s):

Muhammad A. Abdul-Ghani ◽

Tamam Abdul-Ghani ◽

Gabriele Müller ◽

Antje Bergmann ◽

Sabine Fischer ◽

...

Keyword(s):

Plasma Glucose ◽

Roc Curve ◽

Logistic Model ◽

Predictive Power ◽

Glucose Tolerance Test ◽

Tolerance Test ◽

Oral Glucose ◽

Multivariate Logistic Model

Abstract Aim: The aim of this study was to assess the predictive power of glycated hemoglobin (HbA1c) for future type 2 diabetes risk. Research Design and Methods: Six hundred eighty-seven subjects who were free of type 2 diabetes mellitus (T2DM) participated in the study. Each subject received a 75-g oral glucose tolerance test at baseline and 624 received a repeat oral glucose tolerance test after 3.5 ± 0.1 yr of follow-up. Anthropometric measurements, lipid profile, and HbA1c were measured during the baseline visit. Logistic multivariate models were created with T2DM status at follow-up as the dependent variable and other parameters as the independent variables. The receiver-operating characteristic (ROC) was used to assess the predictive discrimination of the various models. Results: HbA1c was a significant predictor of future T2DM risk (area under the ROC curve = 0.73, P < 0.0001). A HbA1c cut point of 5.65% had the maximal sum of sensitivity and specificity. Although the area under the ROC curve of HbA1c was smaller than the area under the ROC curve of both the 1-h plasma glucose concentration and a multivariate logistic model (including anthropometric parameters, lipid profile, and fasting plasma glucose), the addition of HbA1c to both the 1-h plasma glucose and the multivariate logistic model significantly increased their predictive power. Conclusion: Although HbA1c alone is a weaker predictor of future T2DM risk compared with the 1-h plasma glucose, it provides additive information about future T2DM risk when added to previously published prediction models.

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A multivariate logistic model (MLM) for analyzing binary family data

American Journal of Medical Genetics ◽

10.1002/(sici)1096-8628(19980413)76:5<428::aid-ajmg12>3.0.co;2-o ◽

1998 ◽

Vol 76 (5) ◽

pp. 428-437 ◽

Cited By ~ 18

Author(s):

P. Mahinda Karunaratne ◽

Robert C. Elston

Keyword(s):

Logistic Model ◽

Family Data ◽

Multivariate Logistic Model

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Encapsulating Peritoneal Sclerosis in Long-Termed Peritoneal Dialysis Patients

BioMed Research International ◽

10.1155/2018/8250589 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Heng-Jung Hsu ◽

Shih-Ying Yang ◽

I-Wen Wu ◽

Kuang-Hung Hsu ◽

Chiao-Yin Sun ◽

...

Keyword(s):

Peritoneal Dialysis ◽

Medical Records ◽

Treatment Duration ◽

Logistic Model ◽

Clinical Characteristics ◽

Univariate Analysis ◽

Control Group ◽

Dialysis Patients ◽

Multivariate Logistic Model

Background.Encapsulating peritoneal sclerosis (EPS) is a rare but serious clinical complication of long-term peritoneal dialysis (PD) patients with high mortality. The purpose of this study was to assess the clinical characteristics of patients with EPS and to search for possible factors useful for EPS prevention and early diagnosis.Method.This retrospective study was performed in a single dialysis center in Taiwan between August 1990 and April 2014. Overall, a total of 565 patients were included and the medical records of those patients who had developed EPS (EPS group) and those who had not developed EPS (control group) were collected. We compared several factors between these two groups.Result.In the univariate analysis, EPS was significantly associated with a change of transport state (Delta 2) (p= 0.007), duration of PD (p< 0.001), duration of peritonitis treatment (p= 0.001), number of peritonitis episodes (p= 0.002), and fungus related peritonitis (p= 0.031). After multivariate logistic model analysis, we found that only the duration of PD was independently significantly associated with EPS (p= 0.034). In addition, we used the ROC curve and found that a duration of peritoneal dialysis of about 8.4 years is the best cut-off point to predict EPS occurrence.Conclusion.In this study, long-termed PD duration is the only strong independent risk factor for EPS development. Total peritonitis times, total peritonitis treatment duration, and marked increased peritoneal D/Pcrratio were also significantly associated with the duration of PD.

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