Analysising the influence factors of single task pricing based on public packet system: An Empirical Study in China

Sun, Jiangjie; Ping-Wang,; Du, Yanan; Jun-Liu,

doi:10.1088/1742-6596/1437/1/012100

Cited by 8 publications

(10 citation statements)

References 2 publications

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“…The risk perception level was positively correlated with the education, attribute of living place, average annual household income, sub-health duration , number of employees in the unit and sub-health proportion in the unit (P < 0.05), and negatively correlated with age (P < 0.05). Generally, there will be a multicollinearity problem when the correlation coe cient is more than 0.9, and there may be a problem when the correlation coe cient is over 0.8 [31][32] , so 0.6 is the baseline for an acceptable correlation coe cient [33]. In this study, only the correlation coe cient between age and working years or age and the number of children exceeds 0.6, and both correlation coe cients were 0.66.…”

Section: Results Of Correlation Analysismentioning

confidence: 61%

An Empirical Study on Physical Sub-Health Risk Perception: Physical Examination Data of Tertiary Grade-A Hospitals in Anhui Province, China

Jiang¹,

Zhang²,

Gao³

et al. 2021

Preprint

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Background: Physical sub-health is directly related to people's work effectiveness and quality of life. Sub-health prevention has become an urgent medical problem. Current research on sub-health mainly focuses on the diagnosis, influencing factors and treatment. Based on the proposed physical sub-health risk perception scale, we explore the differences in the demographic factors and the mechanism of risk perception level on physical sub-health to provide data support for the management of public physical sub-health.Methods: Clinical and risk perception data were collected from 770 physical examinees, which were analyzed using SPSS 23.0, R, and Python software. Pearson correlation coefficient was used to investigate the correlation and multiple stepwise regression analysis was used to explore the influence of demographic variables on physical sub-health and risk perception. Also, this study used a two-way interaction moderated multiple regression approach to examine the moderating effects of demographic variables on physical sub-health and risk perception.Results: Attribute of living place, sub-health duration, marital status, average annual household income, risk perception level were negatively associated with physical sub-health (r= -0.07 ~ -0.17, P < 0.05). The risk perception level was positively correlated with education, attribute of living place,average annual household income, sub-health duration, number of employees in the unit and sub-health proportion in the unit (r= 0.09 ~0.26, P＜0.05), but negatively correlated with age (r= -0.11, P＜0.05). The number of children had a moderating effect on physical sub-health and risk perception (Interaction coefficient α= -0.3, P＜0.05).Conclusions: The factors directly affecting the physical sub-health included age, education, attribute of living place,sub-health duration and sub-health proportion in the unit, while the risk perception level, marital status and average annual household income were found to be indirect influencing factors. The number of children had a moderating effect on physical sub-health and risk perception. Therefore, we suggest attaching importance to public physical exercise and health checkups, correctly guiding public risk perception, and spreading the national policy of bearing and rearing better children etc. to promote the development of public physical health in China.

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Section: Results Of Correlation Analysismentioning

confidence: 61%

An Empirical Study on Physical Sub-Health Risk Perception: Physical Examination Data of Tertiary Grade-A Hospitals in Anhui Province, China

Jiang¹,

Zhang²,

Gao³

et al. 2021

Preprint

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“…These significant correlations are highlighted in Table 2 . Generally, there could be a multicollinearity problem when the correlation coefficient (r) is above 0.9, 33 , 34 so correlation coefficient of less than 0.6 would be an acceptable baseline for validated analysis. Therefore, Pearson analysis results support study hypothesis 3 (H 3 ) showing treatment satisfaction for medication positively affects the Quality of Life.…”

Section: Resultsmentioning

confidence: 99%

“…Depression was assessed from questions 3, 5, 10, 13, 16, 17, and 21. The total score depression subscale score was subdivided into normal (0-9), mild (10−12), moderate (13−20), severe (21)(22)(23)(24)(25)(26)(27), and extremely severe depression (28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42). Anxiety was assessed from questions 2, 4, 7, 9, 15, 19, and 20.…”

Section: Psychological Health (Depression Anxiety and Stress)mentioning

confidence: 99%

“…The stress subscale was assessed from questions 1, 6, 8, 11, 12, 14, and 18. The total score of stress subscale was subdivided into normal (0−10), mild (11)(12)(13)(14)(15)(16)(17)(18), moderate (19)(20)(21)(22)(23)(24)(25)(26), severe (27)(28)(29)(30)(31)(32)(33)(34), and extremely severe stress (35)(36)(37)(38)(39)(40)(41)(42). The DASS-21 has been demonstrated as valid and reliable for assessing mental health in previous COVID-19 related researches in the Indian population.…”

Section: Psychological Health (Depression Anxiety and Stress)mentioning

confidence: 99%

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Influence of Patient-Reported Treatment Satisfaction on Psychological Health and Quality of Life Among Patients Receiving Divya-Swasari-Coronil-Kit Against COVID-19: Findings from a Cross-Sectional “SATISFACTION COVID” Survey

Balkrishna¹,

Raj²,

Singh³

et al. 2021

PPA

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Introduction The correlation among treatment satisfaction with demographic characteristics, health symptoms or psychological health, and quality of life with the prophylactic regime against COVID-19 is rather unexplored. This real-world exploratory study was conducted to determine patient-perspectives regarding their treatment satisfaction receiving Divya-Swasari-Coronil-Kit with correlative impacts on psychological health (PH) and Quality of life (QoL) based on four hypotheses each relating to PH, QoL, Demographic characteristics, and Treatment satisfaction. Methods This cross-sectional, web-based survey collected data on demographic characteristics and psychological health with DASS-21; QoL with 5-level 5-dimension EuroQol instrument; and treatment satisfaction using Treatment Satisfaction Questionnaire for Medication (TSQM) V9. Pearson correlation coefficient analysis was used to examine the relation between TSQM and PH and the demographic variables. Factor analysis was used for multi-collinearity tests, and multiple linear regression analysis was used to explore demographic variables and TSQM. Results Out of 421 initial screenings, 367 patient-participants were included in the analysis. The mean age of included participants was 33.61 ± 9.47 years. Marital status and socio-economic class positively correlated with TSQM. Physical symptoms in patients are positively correlated with depression, anxiety, and stress; and in contrast, negatively with QoL. Global satisfaction with Divya-Swasari-Coronil-Kit medication negatively correlated with depression, anxiety, stress, effectiveness, convenience; whereas global satisfaction correlated positively with QoL. Conclusion Present study (SATISFACTION COVID) indicates that treatment satisfaction due to avaliablity and treatment of Divya-Swasari-Coronil-Kit has constructive and beneficial implications on psychological health, Quality of life and demographic factors. In addition, web-based patient-reported perspectives may well be a feasible way to provide better insights into treatment satisfaction, in relation to psychological health and Quality of life.

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“…After all questionnaires were reviewed by two graduate students, EpiData3.1 was used to input data, python software was used to explore the status quo of PTP, SPSS19.0 software was used for analysis [26], frequency and percentage of counting data were used for statistical description, and chi-squared test was used to compare the differences of PBPTP among different demographic characteristics. P < 0.05 was considered statistically significant.…”

Section: Methodsmentioning

confidence: 99%

Research on Management of Doctor-Patient Risk and Status of the Perceived Behaviors of Physician Trust in the Patient in China: New Perspective of Management of Doctor-Patient Risk

Sun

Zheng

Jiang

et al. 2020

Mathematical Problems in Engineering

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Based on the situation of physician trust in the patient (PTP), we explored the differences in perceived behaviors of physician trust in the patient (PBPTP). We used the PTP scale as a research tool, taking physicians of the hospitals in Anhui region as the research object to carry out the investigation of PTP, Python software was applied to explore the status of PTP, and the differences of PBPTP distribution rate with different demographic characteristic variables were compared by testing based on theory of planned behavior. We get six results as follows: (1) the overall PTP level was low, and nearly 50% of doctors doubt the integrity of patients. “Patients will not be driven by improper interests” becomes the most reluctant problem or the most distrustful option for doctors. (2) In terms of patients’ participation in disease management and regular follow-up visits, PTP rate in male was higher than that in female (Ps < 0.018). (3) PBPTP was affected by age (Ps < 0.017). (4) In terms of the behavior of patients who did not follow the treatment plans, the PTP rate of postgraduates and above physicians was higher than that of undergraduates and below (P=0.017). (5) In terms of providing diagnosis and treatment information, timely notification of illness, medication information, doctor-patient communication behaviors, and compliance with doctors’ treatment plans, PBPTP was affected by doctors’ professional titles and annual income levels (Ps ≤ 0.001), At the same time, PTP levels of different professional titles showed differences in patients’ respect for doctors’ time and bottom line (Ps ≤ 0.001). (6) In terms of doctor-patient communication behaviors, PBPTP was affected by physician departments (P≤0.001). Hence, demographic characteristics variable may be one of the factors affecting PBPTP, and PBPTP is associated with doctor-patient risk. It makes sense for us to propose a new model of physician-patient risk management from the perspective of PTP about “official-individual-social” triple action.

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Analysising the influence factors of single task pricing based on public packet system: An Empirical Study in China

Cited by 8 publications

References 2 publications

An Empirical Study on Physical Sub-Health Risk Perception: Physical Examination Data of Tertiary Grade-A Hospitals in Anhui Province, China

An Empirical Study on Physical Sub-Health Risk Perception: Physical Examination Data of Tertiary Grade-A Hospitals in Anhui Province, China

Influence of Patient-Reported Treatment Satisfaction on Psychological Health and Quality of Life Among Patients Receiving Divya-Swasari-Coronil-Kit Against COVID-19: Findings from a Cross-Sectional “SATISFACTION COVID” Survey

Research on Management of Doctor-Patient Risk and Status of the Perceived Behaviors of Physician Trust in the Patient in China: New Perspective of Management of Doctor-Patient Risk

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