A study of physician-patient relationship perceived risk measurement of physician: questionnaire development and reliability testing

doi:10.21203/rs.3.rs-1856379/v1

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A study of physician-patient relationship perceived risk measurement of physician: questionnaire development and reliability testing

https://doi.org/10.21203/rs.3.rs-1856379/v1

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Purpose: This study investigates the structure of physicians' perceived risk of physician-patient relationship from physicians' perspective, compiles the physician-patient relationship perceived risk questionnaire, provides a reliable measurement tool for quantitative research on physician-patient relationship in China, and can lay the theoretical foundation for quantitative control of physician-patient relationship risk.

Methods: The initial questionnaire was formed by using literature research, expert correspondence method and applying relevant statistical analysis methods. 553 physicians were conducted to questionnaire survey, and the stability and internal consistency of the questionnaire were tested by item analysis, exploratory factor analysis and other statistical analysis.

Results: Through exploratory factor analysis, we used principal component analysis and the maximum variation method in the orthogonal axis method. The final scale consisted of 4 dimensions and 16 items. The highest loadings of each factor were >0.40. The overall Cronbach's α coefficient was 0.794. All indicators of confirmatory factor analysis and the fitting test of structural model were within the standard range, indicating that the questionnaire has good construct validity. The correlation coefficients of all four factors are less than the square root value of AVE, and absolute values are all less than 0.5, indicating that the factors were correlated and differentiated from each other, which means that the scale data had good discriminant validity.

Conclusion: The physician perceived risk of physician-patient relationship questionnaire developed in this study has good reliability and validity. It can be used as assessment tool to measure the perceived risk of physician in physician-patient relationship.

Physician

Perceived risk

Questionnaire construction

Reliability

Validity

Science and technology are changing in high speed, and new medical devices being put into use have posed new challenges to physicians' medical skills. The rapid increase in social economic production has raised the overall standard of living of the public, and the innovation in medical services cannot fully meet the surging demand of patients for medical services, forming a new conflict in the physician-patient relationship that needs to be alleviated [15, 19]. Risk of the physician-patient relationship (PPR) has become a current research hotspot in the field of social health. PPR is an objectively formed interaction between physicians and patients in the process of medical services, which is the most fundamental of medical interpersonal relationships [24]. Due to the differences in professional environment and subjective conditions between physicians and patients, different expressions and communication skills, it is easy to generate cognitive misalignment and cause various conflicts [26]. The White Paper on the State of Physician Practice in China shows that 66% of physicians in China have personally experienced incidents of physician-patient conflict, 51% of physicians have suffered from verbal violence, and over 30% have experienced violent treatment by patients [3]. How to mitigate or avoid risks of PPR is still an urgent research topic to be addressed today.

Risk perception, which reflects people's cognitive mapping and intuitive judgments of risk, belongs to the category of psychology and was first proposed by Professor Bauer of Harvard University in 1960 [1] and applied to study consumer behavior. There are many definitions of this concept, such as the studies and researches by Paschal Sheeran, Eva Janssen, Fang Lei, Emilie Michalovic, and others [14, 11, 7, 13], who pointed out that risk perception is an individual's judgment and assessment of a specific risk through previous experiences. Therefore, risk perception theory can play an equally important role in the study of the causes of physician-patient risk events and the quantification of risk levels. Integrating various views, we believe that perceived risk can be partially expressed by risk perception variables, which form people's feelings and intuitive judgments about potential risks based on individual subjective experiences. This study is based on risk perception theory and is innovative in exploring a tool for measuring perceived risk of PPR.

In terms of perceived risk measurement, the main focus is on the topic of PPR, which is mainly based on two perspectives: physician and patient. For example, Xue Yuxin et al [22] conducted a review of domestic PPR measurement tools and concluded that trust, loyalty, respect, communication skills, and responsibility are the main measurement dimensions of PPR. The core dimensions of PPR in the narrow sense are physician-patient trust, physician-patient communication, medical technology and service awareness [12]. Foreign scholars have developed PPR scales from different perspectives. Eveleigh RM et al [6] comprehensively analyzed 19 PPR scales, among which, 16 scales were measured from the patient perspective, two scales were measured from the observer perspective, and only one scale (DDPRQ-10) was measured from the physician perspective. The main representative of the scale of the physician's perspective is the DDPRQ-10, a concise scale of poor PPR designed by Hahn SR [10] in 1996 based on the DDPRQ-30. Most domestic measures of PPR have been studied using qualitative research or self-administered questionnaires, and there are no uniform standards [18]. Although Yang Hui et al [23] conducted a scale design in our context based on the DDPRQ-10, the scope of use was limited and the scale reliability and validity needed to be further tested. Ma Shaozhuang et al [12] concluded that the development of a scale suitable for PPR in the Chinese context is pending. Measurement studies that directly address the topic of perceived risk of PPR have not been found. Physicians' perceptions and intuitive judgments of risks in PPR are not only influenced by risk perceptions, but also easily subject to external information. The level of physicians' perception of risk may act as a mediating factor affecting their work attitudes, behaviors, and physical and mental health, thus affecting work quality and even triggering a series of problems such as reduced job satisfaction, psychological stress, and increased propensity to leave [5, 17]. At the same time, whether risks occur may depend on the individual's subjective experience of their perception [20]. Therefore, it is urgent to conduct research on the issue of perceived risk in the medical field from the perspective of protecting physicians.

The physicians' perceived risk of physician-patient relationship (PPRPPR) proposed in this paper refers to physicians' subjective perceptions of the probability of occurrence and severity of various potential risks that may trigger discord between physicians and patients during the treatment process based on their subjective experiences. In recent years, a great deal of research has been conducted at home and abroad on how to "ensure patient safety", but relatively little research has been conducted on the safety of physicians, who are the key players in ensuring patient safety. Moreover, most of these studies are conducted from the perspective of "bystanders" to assess the actual risks in physicians' profession, while physicians themselves, as "insiders", their own perceived risk status of PPR, factors affect their perceived risk level, and the extent to which perceived risks affect their work behavior, these questions are all need to be explored in depth [27]. Therefore, this study develops a PPR risk measurement questionnaire from the physician's perspective to provide a reliable measurement tool for quantitative research on PPR in China, and as a supplement to existing qualitative research, it can lay a theoretical foundation for quantitative control of risk of PPR, improving PPR and building a harmonious society. Physicians in this study included doctors, nurses, and medical staff who have direct contact with patients/families during the course of disease treatment.

Formulation of item pools

The initial items of the questionnaire were mainly derived from literature research and expert interviews. CNKI (China National Knowledge Infrastructure), CBMdisc (China Biology Medicine disc), ScienceDirect (SDOS) and ProQuest were selected as the main search databases, and a total of 52 relevant papers in Chinese and 17 in English were searched. And the related contents of social medicine, health management, health economics and physician-patient risk perception were reviewed.

Based on the literature's explanation of the definition of the perceived risk of PPR and its structure, combined with semi-structured interviews, the more authoritative international Defective Doctor-Patient Relationship Scale (DDPRQ)-10 (medical side perspective), the Physician Trust in Patients Scale, and the Wake Forest Physician Trust Scale in China were used as reference scales for the generation of PPR risk problems for physicians, and 38 items were initially compiled through induction, refinement, and modification in compliance with the principles of avoiding duplication and relevance. To avoid neutral responses and to ensure a clear distinction between attitudes, a 5-point Likert scale was used, with scores from 1 to 5 indicating "not at all", "somewhat", "generally", "fairly", and "completely".

Expert inquiry and modification

The inquiry experts group included associate professors with titles in clinical, nursing, health career management, medical and health policy research and medical psychology specialties to screen and revise the item pool, further revise and form the original questionnaire based on the experts' opinions. According to the screening criteria, items with coefficients of variation greater than 25% and agreement rates < 80% were deleted. After two rounds of discussion, it was agreed that items 1, 3 ~ 10, 12, 22, 29, 31, 34, and 36 were deleted to obtain the original questionnaire with 23 items. The 23 items were combined with questions on general demographic data to form the physician-patient perceived risk questionnaire.

Samples

A stratified random sampling method was adopted to select clinicians, nurses and relevant medical staff in departments of internal medicine, surgery, obstetrics and gynecology, and pediatrics in seven medical institutions in Hefei, Fuyang, and Inner Mongolia. Inclusion criteria were: regular workers in medical institutions; obtaining a license to practice; working for 1 year; willing to participate after being informed of the purpose of the study. Exclusion criteria were: not working in the medical institution during the survey period; not in the medical institution (internship or advanced training); did not want to fill out the questionnaire.

Implement and analysis

We used a self-administered PPRPPR structure questionnaire as the assessment tool, and adopted a combination of online and offline data acquisition, publishing online questionnaires through Questionnaire Star as well as visiting hospitals offline. The paper questionnaires were distributed uniformly to the medical and nursing staffs and managers on duty in each department, and after explaining the requirements, they were asked to fill in the questionnaires independently and to collect and keep them on the spot. The physicians were given explanations for any questions they had while filling out the questionnaire.

Data analysis

Epidata 3.0 software was used for double-checked entry, and SPSS 17.0 and Amos24 statistical software were used for item analysis, factor analysis, and reliability and validity tests.

Demographic characteristics

A total of 553 questionnaires were distributed, and 553 questionnaires were effectively returned (100%). All questionnaires were completed within 20 minutes. Among them, 207 (37.4%) were male and 346 (62.6%) were female; their ages ranged from 18 to 73 years old, with an average age of 35.65 ± 8.84 years old; 322 (58.2%) were in tertiary hospitals, 177 (32.0%) in secondary hospitals and 54 (9.8%) in primary hospitals; 152 (27.5%) were in internal medicine, 76 (13.7%) in surgery, 57 (10.3%) in obstetrics and gynecology, 58 (10.5%) in pediatrics, and 210 (38.0%) in other departments (Table 1).

Table 1

General information about the participate(N=553)
Characteristics	n	%	χ²	p
Gender
Male	207	37.4	34.939	.000
Female	346	62.6	34.939	.000
Age
≤ 30	172	31.1	196.490	.000
31 ~ 40	256	46.3
41 ~ 50	81	14.6
≥ 51	44	8.0
Department
Internal medicine	152	27.5	166.674	.000
Surgery	76	13.7
Obstetrics & gynecology	57	10.3
Pediatrics	58	10.5
Other departments	210	38.0
Education Level
Junior College	92	16.6	286.615	.000
Undergraduate	372	67.3
Postgraduate	89	16.1
Professional Title
Primary	250	45.2	261.503	.000
Middle	211	38.2
Vice-senior	72	13.0
Senior	20	3.6

With the development of modern hospitals and the continuous improvement of the medical and health service system, physician careers have become the choice of more undergraduates and a number of young physicians have emerged. Considering the complexity of many factors such as environment, the incidence of physician-patient risk is higher in tertiary hospitals, so the number of physicians in tertiary hospitals selected was higher. The collected data match the current situation of physician-patient risk in China and are suitable for this study.

Item analysis

A combination of mean and standard deviation (SD) was used to reflect the degree of dispersion (x ± s) among individuals within the group, and to describe the distribution characteristics of the observations in terms of both the mean and the degree of variability, with a smaller standard deviation implying a smaller degree of dispersion of the observations, i.e., a greater degree of variability. It is generally considered that when the standard deviation is less than 0.8, the degree of differentiation is poor and should be removed [8]. According to this criterion, the standard deviation of item 21 was less than 0.8, so it was deleted (Table 2).

Table 2

Results of the discrete trend method analysis
Item	‾x ± s	Item	‾x ± s	Note
T1	3.65 ± 0.876	T13	1.75 ± 1.058
T2	3.42 ± 1.035	T14	2.86 ± 1.034
T3	2.82 ± 0.965	T15	3.53 ± 1.018
T4	3.16 ± 0.943	T16	3.07 ± 1.001
T5	3.79 ± 0.841	T17	2.94 ± 1.091
T6	4.09 ± 0.804	T18	4.14 ± 0.949
T7	3.69 ± 0.929	T19	3.31 ± 0.876
T8	3.96 ± 0.892	T20	3.53 ± 0.825
T9	2.86 ± 1.072	T21	3.48 ± 0.742	Deleted
T10	2.84 ± 1.108	T22	3.11 ± 0.820
T11	1.79 ± 1.119	T23	3.09 ± 0.921
T12	2.63 ± 1.097

The subjects with 27% of the high and 27% of the low scores were divided into high and low subgroups by ranking the total scores of each subject from high to low after scale summation, and the differences between the two groups in the scores of each item were analyzed using independent-sample t-test. If the absolute value of the critical ratio was less than 3.0 or the difference in the critical ratio was not statistically significant (p > 0.01), indicating that the items were not homogeneous with the overall scale, the discriminatory power was considered insufficient to identify the degree of response of different subjects, and they were deleted. The analysis revealed that the absolute values of the critical ratio of the 23 items of the pretest questionnaire were all greater than 3, and p < 0.01, indicating that each item of the questionnaire had a good discriminatory power.

The Pearson's Correlation Coefficients (r) method was used to analyze the correlation between the items and the total score of the initial questionnaire; the larger the absolute value of the correlation coefficient, the stronger the correlation; the closer the correlation coefficient is to 0, the weaker the correlation. The correlation strength of the variables is usually judged by the following range of values: correlation coefficient 0.8 ~ 1.0 is highly correlated; 0.6 ~ 0.8 is strongly correlated; 0.4 ~ 0.6 is moderately correlated; 0.2 ~ 0.4 is weakly correlated; 0.0 ~ 0.2 is very weakly correlated or uncorrelated. Items with r less than 0.4 were deleted [8]. Based on the above criteria, items 10, 13, and 18 were deleted (Table 3).

Table 3

Results of Pearson correlation coefficient analysis
Item	r	Note	Item	r	Note
T1	0.423^**		T13	0.332^**	Deleted
T2	0.457^**		T14	0.455^**
T3	0.463^**		T15	0.465^**
T4	0.462^**		T16	0.401^**
T5	0.486^**		T17	0.431^**
T6	0.473^**		T18	0.350^**	Deleted
T7	0.578^**		T19	0.531^**
T8	0.519^**		T20	0.495^**
T9	0.479^**		T21	0.527^**
T10	0.377^**	Deleted	T22	0.495^**
T11	0.415^**		T23	0.424^**
T12	0.414^**
Note: ^**p < 0.01

Exploratory factor analysis

The Kaiser-Meyer-Olkin (KMO) test statistic is an indicator used to evaluate the correlation coefficient between variables [16]. The common measure of KMO value: when performing factor analysis, the KMO value should be ≥ 0.6, and when the KMO value is ≥ 0.8, it indicates a good fit. In this study, the KMO test and Bartlett's test were performed on the initial questionnaire containing 23 items, and the KMO value: 0.841 and the Bartlett's spherical test chi-square value reached a significant level (p < 0.000), indicating that the data of this study were suitable for factor analysis.

Principal component analysis was used to calculate the correlation matrix of the questionnaire variables, and common factors with eigenvalues greater than 1 were extracted by the maximum variation method in the orthogonal transcendental method. The reference criteria for retention and deletion of items in this study were [2]: (1) items with maximum loadings less than 0.4 in factor analysis; (2) items containing less than three common factors; (3) items with factor loadings spanning two and more. Based on the above criteria, items 11, 12, and 15 were deleted (Table 4).

Table 4

Factor analysis rotated component matrix
Item	Components
	1	2	3	4	5
T6	.796
T8	.770
T7	.755
T5	.721
T20		.824
T19		.766
T22		.643
T23		.566
T3			.739
T2			.633
T4			.549
T1			.447
T17				.638
T16				.618
T14				.576
T11			.517	.548
T9				.494
T15					.775
T12			.491		.510

After exploratory factor analysis, items 10 ~ 13, 15, 18, and 21 were removed for a total of 7 items. The final result was a PPRPPR questionnaire consisting of 4 factors and 16 items. Four common factors were basically consistent with the PPR perceived risk framework constructed in this study. Based on professional knowledge and expert opinions, the four factors were named as risk perception, physician-patient trust, responsibility perception and organizational management after collective discussion by the group.

Reliability and validity

Content validity

In this study, Cronbach's α coefficient was used to test the internal consistency of the PPRPPR questionnaire. Reliability coefficients between 0.6 and 1.0 can be considered more reliable [9], and the total Cronbach's α coefficient of this questionnaire is 0.794, indicating that the questionnaire has good reliability. Six experts from the fields of health administration, nursing research and psychology were invited to evaluate the content validity of the questionnaire and agreed that the questionnaire has good content validity.

Construct validity

Confirmatory Factor Analysis (CFA) was used to test whether the factor model of the PPRPPR questionnaire was consistent with the theoretical framework constructed in the previous study. Among the evaluation indicators of CFA, the χ² value is the most commonly used test of goodness of fit, and the smaller the chi-square test and the χ² value, the better the fit. The ratio of the χ² value to the degree of freedom df is usually used to indicate the probability of correctness of the structural model, and the smaller the ratio, the better the model fit. χ² /df < 5 means that the structural model is acceptable, and χ² /df < 3 means that the model fit is better. Besides, Goodness of fit index (GFI), Adjusted goodness of fit index (AGFI), Comparative fit index (CFI), Root mean square error of approximation (RMSEA), Incremental fit index (IFI), Parsimony normed fit index (PNFI), Parsimony goodness of fit index (PGFI) and other metrics are often used in studies to measure the fit of factor models. The variation interval of GFI, AGFI, CFI, IFI, and RMSEA is between 0 ~ 1. The closer the GFI, CFI, IFI, and RMSEA are to 1, the better the fit is; AGFI > 0.80 is considered a better model fit; RMSEA should be less than 0.08, and the smaller its value is, the better the fit is [25]; PNFI and PGFI > 0.50 are considered good model fits [21]. The confirmatory factor analysis was performed using Amos24 software to test the fit of the structural model of the questionnaire. All indicators of the PPR risk factor model were within the standard range (Table 5). This indicates that the structural model of PPRPPR consisting of the four factors has a good fit. Therefore, the structural validity of the physician-patient relationship risk questionnaire is good (Fig. 1).

Table 5

Confirmatory factor analysis
Indicators	Recommended Values	Fitted values
χ²/df	< 5.0	3.097
GFI	> 0.9	0.934
AGFI	> 0.8	0.908
CFI	> 0.9	0.900
RMSEA	< 0.08	0.062
IFI	> 0.9	0.901
PGFI	> 0.5	0.673
PNFI	> 0.5	0.703

risk of physician-patient relationship questionnaire

Discriminant validity

Table 6 was obtained by confirmatory factor analysis. It was patently obvious that the four factors have significant correlation (p < 0.01), and the correlation coefficients are all less than the square root value of AVE, and absolute values are all less than 0.5. The results showed that the factors were correlated and differentiated from each other, which means that the scale data had good discriminant validity [4].

Table 6

Discriminant validity: Pearson correlation and AVE square root value
Factor	F1	F2	F3	F4
F1	0.761
F2	0.115^**	0.707
F3	0.185^**	0.067^**	0.602
F4	0.058^**	0.102^**	0.052^**	0.584
Note: ^**p < 0.01; the shaded part is the square root value of AVE extracted from the average variance.

Based on the basic principles of general questionnaire development, the physician perceived risk of physician-patient relationship questionnaire was developed through literature review, expert consultation, group discussion, questionnaire survey, and statistical analysis to form the initial questionnaire. The questionnaire was distributed to 553 clinicians, nurses and managers of related departments in seven medical institutions in Hefei, Fuyang and Inner Mongolia, including internal medicine, surgery, obstetrics and gynecology, and pediatrics, with a valid return rate of 100%, indicating that most physicians were able to respond positively and the questionnaire was acceptable. To revise and validate the questionnaire, items were screened by SPSS and Amos24 software for item analysis and exploratory factor analysis to construct a PPR perceived risk factor model; reliability and validity tests were conducted to examine the stability and internal consistency of the questionnaire to ensure the scientificity and accuracy. The overall Cronbach's α coefficient of the questionnaire in this study was 0.794, indicating good internal consistency. Through exploratory factor analysis, the structural model of PPRPPR was constructed by using principal component analysis and the maximum variance method in the orthogonal axis method and extracting common factors with eigenvalues greater than 1. It contained four factors: risk perception, physician-patient trust, responsibility perception and organizational management, and the highest loadings of the items of each factor were > 0.40. The best fit of the factor model was sought through confirmatory factor analysis to test whether its questionnaire structure was consistent with the theoretical framework constructed in the previous study. The results showed that χ² /df (3.097) < 5, GFI (0.934) > 0.9, AGFI (0.908) > 0.8, CFI (0.900) > 0.9, RMSEA (0.062) < 0.08, IFI (0.901) > 0.9, PGFI (0.673) > 0.5, PNFI (0.703) > 0.5, all indicators were within the standard range, indicating that the questionnaire has good construct validity. The correlation coefficients of all four factors are less than the square root value of AVE, and absolute values are all less than 0.5, indicating that the factors were correlated and differentiated from each other, which means that the scale data had good discriminant validity.

In summary, this study aims to develop a risk assessment questionnaire suitable for Chinese physicians' perceived risk of physician-patient relationship, through which physicians can understand their own risk perception level and provide a theoretical basis for their accurate understanding and effective avoidance of occupational risks and increase their sense of occupational safety. It has important theoretical value and practical significance for hospital management organizations to further improve the quality of current physician risk management and reduce medical disputes. At the same time, it can meet the needs of risk management and research in physician groups and make the results of similar studies more comparable in the future.

PPR: Physician-Patient Relationship;

PPRPPR: Physicians' Perceived Risk of Physician-Patient Relationship

CFA: Confirmatory Factor Analysis

AVE: Average Variance Extraction

Ethics approval and consent to participate

This is an observational study. On the first page of the questionnaire we provided participants with a detailed description of the background, purpose and nature of this questionnaire. Participants were informed about the study and then they agreed to participate and provided a written informed consent. The questionnaire was used for academic research only and participants were assured that their information would not be disclosed at any time. The Anhui Medical University Research Ethics Committee has confirmed that no ethical approval is required. All methods were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki).

Consent to publish

Not applicable.

Availability of data and materials

To ensure full anonymity for the participants the transcribed interviews are not possible to share publicly.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported in part by the Natural Science Foundation of Anhui Province of China (No.1908085MG233), Quality Engineering for research projects of the Anhui Department of Education (Nos. 2020SJJXSFK1341 and 2020wyxm108), the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China (Nos. KJ2021A1228 and KJ2021A0266), Projects for National Health Insurance Agency of the China (2021056), Anhui Medical University School of Health Management "Advanced Mathematics for Medical Purposes" Course Civics Project.

Authors' Contributions

Yufei Gao conducted the study and wrote the manuscript under thorough supervision of Tong Liu and Hui Li. Material preparation, data collection and analysis were performed by Yufei Gao. Jiangjie Sun and Liping Zhang guided the writing and suggested revisions. All authors read and approved the final manuscript.

Acknowledgements

The authors thank all study participants for their contribution to the research.

Authors' Information

¹Health Management College, Anhui Medical University, Hefei, Anhui Province, P.R. China. ²School of Marxism, Anhui Medical University, Hefei, Anhui Province, P.R. China.

Bauer, R. A. (1960). Consumer behavior as risk taking. Dynamic Marketing Changing World, 398.
Cheng, L., Feng, C., Wei, X., Du, L., Xu, Z. (2011). The development of questionnaire on public satisfaction with basic education in Beijing. Journal of Educational Studies, 7(3):85–91 + 104.
Chinese Medical Doctor Association. (2018, January 10). The White Paper on the State of Physician Practice in China. Retrieved from https://wenku.baidu.com/view/324b7f5332b765ce0508763231126edb6f1a76d5.html
Chi, X., Heesup, H. (2020). Exploring slow city attributes in Mainland China: tourist perceptions and behavioral intentions toward Chinese Cittaslow. Journal of Travel and Tourism Marketing, 37(3): 361–379. https://doi.org/10.1080/10548408.2020.1758286
Deng, G., Cai, W., Yang, M., Lio, J., Feng, C., Ma, X., Liang, L. (2021). Linking doctor-patient relationship to medical residents' work engagement: The influences of role overload and conflict avoidance. BioMed Central Family Practice, 22(1):191. doi:10.1186/s12875-021-01541-6
Eveleigh, R. M., Muskens, E., van Ravesteijn, H., van Dijk, I., van Rijswijk, E., Lucassen, P. (2012). An overview of 19 instruments assessing the doctor-patient relationship: different models or concepts are used. Clinical Epidemiology, 65(1):10–15.
Fang, L. (2015). The study of questionnaire construction of risk perception and its influence factors for common chronic inpatients. Four Military Medical University.
Fang, L., Ren, P., Zhang, Y., Cao, B. (2014). Primary development of risk perception questionnaire for chronic patients. China Journal of Health Psychology, 229(12):1865–1867.
Gao, M. (2014). Development of students weariness situation inventory. China Journal of Health Psychology, 22(2):198–200.
Hahn, S.R., Kroenke, K., Spitzer, R.L., Brody, D., Williams, J.B., Linzer, M. and deGruy, F.V. (1996). The difficult patient: prevalence, psychopathology, and functional impairment. Journal of General Internal Medicine, 11(1):1–8.
Janssen, E., van Osch, L., de Vries, H., Lechner, L. (2011). Measuring risk perceptions of skin cancer: reliability and validity of different operationalizations. British Journal of Health Psychology, 16(1):92–112.
Ma, S., Xu, X., Trigo, V., Ramalho, N. J. (2017). Doctor-patient relationships (DPR) in China. Journal of Organization and Management, 31(1):110–124.
Michalovic, E., Hall, S., Duncan, L. R., Bassett-Gunter, R., Sweet, S.N. (2018). Understanding the Effects of Message Framing on Physical Activity Action Planning: the Role of Risk Perception and Elaboration. International Journal Of Behavioral Medicine, 25(6):626–636.
Sheeran, P., Harris, P. R., Epton, T. (2014). Does heightening risk appraisals change people's intentions and behavior? A meta-analysis of experimental studies. Psychological Bulletin, 2014;140(2):511–543.
Sun, J., He, C., Zhang, L. (2018). Related factors of physicians trust in patients. Chinese Mental Health Journal, 32(05):407–409.
Sun, J., Jiang, X., Gao, Y., He, C., Wang, M., Wang, X., Yu, Z., Si, X., Shi, X., Zhang, L. (2022). "Subhealth Risk Perception Scale: Development and Validation of a New Measure", Computational and Mathematical Methods in Medicine, vol. 2022, Article ID 9950890, 13 pages, https://doi.org/10.1155/2022/9950890
Sun, J., Sun, R., Jiang, Y., Chen, X., Li, Z., Ma, Z., Wei, J., He, C., Zhang, L. (2020). The relationship between psychological health and social support: Evidence from physicians in China. PLoS ONE 15(1):e0228152. https://doi.org/10.1371/journal.pone.0228152
Sun, J., Zhang, L., Mu, P., Wan, Y., Liu, J., He, C. (2016). Difference in the cognitive evaluation of the doctor-patient relationship between the medical side and the contracting parties. Chinese Mental Health Journal, 30(07):486–491.
Sun, J., Zhang, L., Sun, R., Jiang, Y., Chen, X., He, C., Wei, J. (2018). Exploring the influence of resiliency on physician trust in patients: An empirical study of Chinese incidents. PLoS ONE,13(12):e0207394. https://doi.org/10.1371/journal.pone.0207394.
Sun, J., Zheng, Z., Jiang, X., Hu, W., Zhang, L. (2020). 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. Mathematical Problems in Engineering, (1):1–8. https://doi.org/10.1155/2020/2145029
Wu, M. (2003). SPSS Statistical Application Practice - Questionnaire Analysis And Application Statistics. Science Press.
Xue, Y., Liang, L., Gao, L., Kang, Z., Li, Y., Song, W., Shan, L. (2018). Literature study on the development of scales for doctor-patient relationship. Chinese Hospital Management, 38(2):69–71.
Yang, H., Wang, H. (2011). Assessment of the reliability and validity on the Chinese edition of Doctor-Patient Relationship Scale PDRQ-15. Chinese Medical Ethics, 24(03):350–353.
Yang, K. (2018). The legal protection of patients' rights in doctor-patient relationship. Southwest University of Political Science and Law.
Yang, X., Jiang, X., Zhang, Y., Sun, L., Wang, C., Shang, L. (2012). Development and evaluation of preschooler's eating behavior scale. Chinese Journal of Child Health Care, 20(08):682–685.
Zhang, X. (2021). Self and Society: An Examination of the Doctor-Patient Relationship in the Context of Symbolic Interactionism. Modern Communication, (04):230–232
Zhang, X. (2016). Study on questionnaire construction of risk perception and influence factors analysis for nurse. Fourth Military Medical University.

Physician Perceived Risk of Physician-patient Relationship Scale

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A study of physician-patient relationship perceived risk measurement of physician: questionnaire development and reliability testing

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Abstract

Figures

Introduction

Samples And Methods

Formulation of item pools

Expert inquiry and modification

Samples

Implement and analysis

Data analysis

Results

Demographic characteristics

Item analysis

Exploratory factor analysis

Reliability and validity

Content validity

Construct validity

Discriminant validity

Conclusion

Abbreviations

Declarations

References

Appendix

Status:

Version 1