Determinants of primary care physicians’ intention to provide Breast cancer screening services for rural women: a structural equation model based on the theory of planned behavior

doi:10.21203/rs.3.rs-3908535/v1

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Determinants of primary care physicians’ intention to provide Breast cancer screening services for rural women: a structural equation model based on the theory of planned behavior

https://doi.org/10.21203/rs.3.rs-3908535/v1

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Background

Breast cancer has been a serious health problem all around the world. Early detection is undoubtedly effective in combating severe public health problems in developing countries. Meanwhile, primary care physicians play an important role in implementation of screening programs. The objective of our study was to evaluate the determinates of the primary care physicians’ intention to provide the Breast cancer screening services ( BCSs ) for rural women.

Methods

We conducted a cross-sectional survey in 24 towns in Jiangsu. In total, 1,101 primary care physicians entered and completed the study. The data collection tool was developed based on the theory of planned behaviour (i.e., attitude, subjective norms, and perceived behavioral control) and extended components (i.e., knowledge about breast cancer screening services (BCSs), and past providing-BCSs behavior.

Results

The results of our study showed that subjective norms (β = 0.352, p < 0.001) had the greatest influence on primary care physicians’ intention to engage in breast cancer screening, followed by attitudes and perceived behavioural control. Both screening knowledge and past screening provision behavior had an indirect effect on behavioural intentions.

Conclusions

The present study demonstrated that extended TPB appears to be an efficient model in explaining primary care physicians' intention to engage in Breast cancer screening program. Meanwhile, our findings provide a reference for governments, hospitals and policies aiming to increasing primary care physicians’ intention to provide rural women with BCSs.

Breast cancer

primary care physicians

theory of planned behavior (TPB)

intention

structural equation modeling

Not only is breast cancer a malignant tumor accompanied with an extraordinarily high incidence rate, but also is a public health problem related to women's life quality as well. In recent years, breast cancer has ranked top on the incidence rates of cancers and has accounted for 11.7% of the total newly diagnosed cancer cases (1). Meanwhile, because of limited medical resources and opportunities, it has been proved that rural women were more likely to develop breast cancer than urban women(2). It appears that breast cancer has been a major public health problem globally, especially among the rural women. As the economy develops and people's way of life changes, the prevalence of breast cancer in China has increased dramatically over the past three decades (3), research shows that the incidence of breast cancer among Chinese rural women was 79 per 100,000 in 2015 (2) (4). Early screening, especially organized screening program is crucial for treatment and prognosis an can not only reduce the incidence and mortality of breast cancer, but also improve women's follow-up and quality control(5). Despite the launch of NCCSPRA, a free National Breast Cancer Screening Program in Rural Areas starting from 2009 in China for rural women aged between 35–64, women’s intention of taking part in breast cancer screening is unsatisfying, the age-standardized analysis indicated that 3-year breast cancer screening rates in 2015 among females in China aged 40–69 years old were 21.4%(6), much lower than developed countries such as United Kingdom (75.9%) (6)and United States (72.4%)(7). According to Weinberger et al(8), barriers to breast cancer screening include expensive cost and fear of pain. Other factors include uneven distribution of economic and clinical resources(9). Therefore, there’s an urgent need to adopt appropriate measures to mitigate negative effects of breast cancer and improve rural women’s health in China.

Primary care physicians serving as frontline workers of hierarchical diagnosis and treatment influence rural women's awareness of engaging in the screening program(10, 11). Research have already demonstrated that primary healthcare workers exert impact on women’s participation of cancer screening(12) (13), another study (16) suggested that the quality of physician communication skills is an important predictor that patient will undergo a screening mammography. These studies suggested that research on physicians’ involvement be a possible angle to improve breast cancer screening services(BCSs). However, most previous research have focused on the women’s angle of breast cancer. For example, there are some studies(14) (15) explored women’s intention of engage of BCSs. Worldwide, few studies have explored primary care physicians’ intention of providing BCSs using theories related to health behaviour. Therefore, it’s important to investigate the predictors of primary care physicians’ intention to provide BCSs for rural women.

To understand the physicians’ breast cancer screening behavior, our research is based on Ajzen’s theory of planned behavior (TPB)(16). The TPB is ideally suited to help explore predictors of physicians’ BCSs intention through this study. The strength of the TPB is that it’s a widely accepted cognitive theory which provide a framework to understand individuals’ action and is proved to be useful in predicting the intention of primary care physicians to engage in specific medical behavior. For instance, Alradini et al. (17) found out that primary physicians from Saudi Arabia are lack of awareness of anti-biotic resistant using TPB, and Liu et al.(18), during their study of prescriptions of primary physicians in China, corroborated the efficacy of TPB. Therefore, this study applied the TPB model to explain primary care physicians’ intention to engage in BCSs. Our study objectives are listed as follows: (a) to investigate factors which affect primary healthcare physicians’ intention to engage rural women in breast cancer screening programs; (b) to provide practicable suggestions for future policies.

The current study established the model of primary physicians’ provision of breast cancer screening for rural women in China on the basis of TPB. TPB is extensively used in predicting and explaining an individual’s behavior under certain circumstances and is proved to be effective while anticipating the intention of clinicians (19), pharmacists (20) and nurses (21) to offer medical services. According to TPB, individual’s intention is determined by attitude toward the behavior (A), subjective norm (SN) and perceived behavior control (PBC). Moreover, SN can affect attitude and PBC, thus indirectly affected individual’s intention (Fig. 1).

Attitude refers to an individual's stable assessment and stance on a particular behavior. Attitude can be influenced by strength and belief (b) and evaluation (e), and can be quantized by their multiplication(16, 22). The equation is as follows: \(AB\propto \sum {b}_{i}{e}_{i}\)(i means the measurement project). Similarly, subjective norms refers to the belief that a people will approve and support a particular behavior, which can be determined by normative belief (n) and motivation to comply (m). The equation is as follows: SN\(\propto \sum {n}_{i}{m}_{i}\). Moreover, PBC refers to the extent to which one measures the difficulty of conducting a specific behavior, which is determined by two distinct factor: control beliefs (c) and perceived power (p), and the equation is as follows:\(\text{P}\text{B}\text{C}\propto \sum {c}_{i}{p}_{i}\)

In our study, A refers to primary physicians’ evaluation of providing BCSs. According to TPB, A is an important factor which influences an individual’s behavior. Physicians’ intention of providing breast cancer screening will be inspired if they have a sense of achievement or the realization of their personal value, or they approve the importance of breast cancer screening themselves. Allenbaugh et al. (23) indicated that nurses' and residents’ attitude towards their interaction can significantly improve the quality of nursing. Thus, we make the following hypothesis:

Hypothesis 1

Attitude is positively correlated with primary physicians’ intention to provide BCSs.

In our study, SN refers to the extent to which people closely related to primary physicians support their work. SN not only exerts direct effects on intention, but also influences behavior intention through attitude and perceptual behavior control indirectly. For instance, attitude of leaders in hospitals, nurses and other colleagues might influence primary physicians' attitude towards BCSs, and may reduce their difficulties while conducting BCSs, and further stimulate their motivation. Therefore, we make the following hypothese:

Hypothesis 2

SN influences primary physicians' intention positively and directly.

Hypothesis 3

The more SN primary physicians’ sense, the more positive their attitude becomes.

Hypothesis 4

SN significantly affects the perceptual behavior control of primary physicians during their attempt to provide BCSs.

In our study, PBC refers to the complexity to which primary physicians provide breast cancer screening(e.g, whether there are sufficient facilities, whether physicians' skills are mastered). In general, the more resources physicians have, the more confident they are with BCSs, the stronger their PBC is. According to TPB, PBC is significantly connected with an individual's intention. Other research has figured out that TPB is a predictor of physicians' intention to provide medical services (24). As a result, we make the following hypothesis:

Hypothesis 5

PBC influences primary physicians' intention of providing breast cancer screening directly and positively

It has been confirmed that the predictive utility of TPB can be improved by adding other variables(25, 26). Meanwhile, Past behavior (PB) and knowledge (KN) had been added to the TPB model in health-related studies, which supported the inclusion of PB and knowledge as a variable to improve the predictive power of the TPB (27–29). For primary physicians, knowledge can affect their intention through PBC. For instance, the more knowledge they have, the more they are aware of the benefits of PBC, thus are more willing to providing this service. Knowledge can also reduce their sense of complexity of breast cancer screening(e.g. they are more skillful if they conduct breast cancer screening frequently). Moreover, people around them may be affected as well, since they learn more about this medical behavior, and consequently offer more support to primary physicians. As a result, we make the following hypotheses:

Hypothesis 6

knowledge of breast cancer screening influences primary physicians’ attitude toward breast cancer screening directly and positively.

Hypothesis 7

knowledge of breast cancer screening influences primary physicians’ SN of providing breast cancer screening directly and positively.

Hypothesis 8

primary physicians’ behavior to provide breast cancer screening in the past influences their SN.

Hypothesis 9

primary physicians’ behavior to provide breast cancer screening in the past influences their PBC.

Hypothesis 10

primary physicians’ behavior to provide breast cancer screening in the past influences their knowledge of BCSs.

2.1 Participants and data collection

We conducted a cross-sectional survey from March 30 to June 1, 2020. Multi-stage stratified cluster sampling method is used to determine the survey subjects. In the first stage, Jiangsu Province is taken as the primary sampling unit, and the geographical location, socioeconomic level, distribution of medical resources and other conditions are also comprehensively considered. Finally, 6 cities (Lianyungang, Yancheng, Yangzhou, Nanjing, Changzhou and Wuxi) which sited in northern, central, and southern of Jiangsu were selected respectively. Further, considering the population size of each city, the probability proportionate to size (PPS) method was utilized to select 2 counties in our selected distinct, then 2 towns were selected from each county. Physicians working in the 24 selected town’s public health care institutions (such as maternity and child care institutions, community health service centers, health service stations, township health centers and village clinics) were included in the survey. We included physicians if they have medical qualification and previous experience in breast cancer screening services(including. We exclude medical staff if they : 1) are pharmaceutical, testing or nursing staff in medical institutions; 2) have no experience in breast cancer screening services. We collaborated with hospitals, all participants were gathered to finish our questionnaires with the help of our trained research teams. All subjects have given informed consent.

2.2 Questionnaire

Based on our hypothesis model, we referred to existing literature (30–32) and used various tools to enrich our study and amended our questionnaire. To ensure that the theoretical constructs(A, SN, PBC, PB, KN, and intention) were appropriately represented in the instrument, the initial questionnaire was first evaluated through a focus group (including two researchers with experience in quantitative research as well as three primary care physicians). They were also asked to assess on how to improve the validity of the questionnaire. Based on their feedback, the wording in the questionnaire was adjusted to improve clarity. Then, we conducted a pilot test using the revised questionnaire on a sample of 30 primary care physicians. Finally, we adjusted the content of our questionnaire according to the feedback of the pilot test, and finally accomplished the questionnaire on the intention of primary physicians to provide breast cancer screening. Our questionnaire includes two parts: 1)demographic and sociological characteristics (gender, monthly income, education background, etc.). 2) the intention to provide breast cancer screening, the latter containing 6 subscales.

2.2.1 Attitude Subscale

This subscale was consisted of behavior intention (3 items) and related outcome evaluation (3 items), which included three components: the awareness of BCSs’ value, the satisfaction, and the fulfillment of providing BCSs. Respondents rated on a five-point Likert scale, with a higher score indicating more favorable attitude toward BCSs.

2.2.2 Subjective norm Subscale

The SN was calculated by multiplying the products of “normative beliefs”(3 items) and “motivation to comply” (3 items). The respondents were asked to rate the degree of support of their supervisors, colleagues, and visiting women on a five-point rating scale. Higher scores were closely associated with a higher SN.

2.2.3 Perceived behavioral control Subscale

Perceived behavioral control refers to the perception of hindering or facilitating factors and reflects personal resources which enable primary care physicians to engage in BCSs. It included the efficacy of the hospital equipment, workload, salary and working skills. Respondents rated on a five-point Likert scale, with a higher score indicating higher PBC of primary care physicians in performing BCSs.

2.2.4 Behavior intention Subscale

BI toward BCSs was measured by three items: “I plan to provide BCSs for rural women” “I am willing to provide BCSs for rural women” and “I try to provide BCSs for rural women”. The response scale ranges from 1 (strongly disagree) to 5 (strongly agree) with higher scores indicating that physicians have greater intention to engage in BCSs program.

2.2.5 Knowledge Subscale

The index for knowledge of breast cancer screening to reveal the status of breast cancer screening knowledge among primary care physicians consists of six items. All items were scored with right = 1, wrong = 0. A sum score was computed. The total score ranged from 0 to 6, and a higher score indicates a higher level of primary care physicians’ BCSs related knowledge.

2.2.6 Past Behavior Subscale

We used four items to measure primary care physicians’ past behavior of performing BCSs, including providing clinical examination, mammography, ultrasonography, and preventive education for rural women. The response scale ranges from 1 (strongly disagree) to 5 (strongly agree) with higher scores indicating that physicians have more experience of providing BCSs for rural women.

2.3 Data and Statistical Analysis

The database was created using EpiData 3.1 software. Firstly, all data were subjected to descriptive statistics and exploratory factor analysis using SPSS 23.0. (IBM Corp., Armonk, NY, USA). The model was initially constructed using exploratory factor analysis, and correlations between variables were analyzed using Kaiser–Meyer–Olkin (KMO) test and Bartlett's spherical test. The confirmatory factor analysis (CFA) was then carried out using SEM to evaluate the reliability and stability of the model. Finally, Amos 23.0 (IBM SPSS Amos, Armonk, NY, USA) was used to fit the hypothetical research model and the relationships between the variables using the Maximum Likelihood method, with a difference considered statistically significant at P < 0.05.

3.1 Sample characteristics

A total of 1,205 questionnaires were distributed, and 1,101 valid questionnaires were recovered, with a rate of 91.37%. The questionnaires with invalid and incomplete responses were removed. The sample composition is shown in Table 1. Participators were mainly female (90.7%). Most (79.8%) of them were from community health institutions and township health centers, and the majority (62.5%) of them were at the undergraduate level, followed by junior college graduates (27%). All information is listed in Table 1.

Table 1

Demographics and relevant characteristics of participants
Demographic variables	Frequency (N)	Percentage (%)
Gender
Male	102	9.3
Female	999	90.7
Monthly income(RMB)
≤ 3000	112	10.2
3000—5000	373	33.9
5000—8000	479	43.5
≥ 8000	137	12.4
Level of Education
Doctor	1	1
Master	26	2.4
Bachelor	688	62.5
Associate degree	297	27
others	89	8.1
Type of hospital
Township Health Center	431	39.1
Village clinic	76	6.9
Rural Community Health Center	448	40.7
Rural Maternal and Child Health Center	91	8.3
other	55	5

3.2 Descriptive analysis

Primary care physicians in this study showed a rather favorable attitude toward providing BCSs to rural women; the mean score of variable Attitude was 18.79 ± 4.53 ( score ranging from 1–25). Meanwhile, our participants didn’t feel that they gained much support from their colleagues, patients, and leaders during their daily practice of BCSs (Mean = 17.00, SD = 3.94, score ranging from 1–25), their perception of behavioral control of engaging in BCSs was negative (mean = 11.18, SD = 3.76, score ranging from 1–25). The present study showed that primary care physicians have a relatively high intention to provide BCSs for rural women (mean = 4.07, SD = 0.63, score ranging from 1–5), a good knowledge level of BCSs were also evident with a mean score of 3.93 ± 1.28 ( scores ranging from 0–6). Also, our participants didn’t have much experience of providing BCSs for rural women (mean = 12.43, SD = 3.70, score ranging from 4–20).

3.3 Instrument reliability and validity

SPSS23.0 was used to conduct exploratory factor analysis on the collected data to verify the validity and reliability of questionnaire data. Firstly, factor analysis was carried out. The KMO value was 0.858 > 0.5, the approximate chi-square value of Bartley spherical test was 8719.532, and the significance probability was less than 0.001. It shews that the data were very suitable for factor analysis (33). In this study, the principal component analysis method was used to extract common factors, and the maximum variance orthogonal rotation method was used to conduct data analysis. Finally, 4 common factors with characteristic root over 1 were obtained, and the cumulative contribution rate was 77.038%. Factor loadings of each item ranged from 0.716 to 0.882.

Confirmatory factor analysis mainly includes construct, convergent and discriminant validity. AMOS 23.0 was used for data processing, and the maximum likelihood method was used to evaluate the fit of the scale in our study. The results showed that χ2/ DF ratio was 3.114 < 5, RMSEA (root mean square error of approximation) was 0.044 < 0.05, SRMR (standardized root mean square residual) was 0.0337 < 0.08. CFI (comparative fit index), TLI (Tucker-Lewis index) and NFI (normed fit index) are all higher than 0.90, indicating that the model had good structural validity and satisfactory fitting (34). In addition, it can be seen from Table 2 that standardized path coefficients of the four latent variables corresponding to each topic are all above 0.5, and the average variance extracted from each latent variable is higher than 0.5 (35), and the combined reliability is higher than 0.7(36), indicating that the convergence validity is ideal. In addition, as is shown in Table 3, Attitude, SN, PBC and behavior intention have significant correlation (P < 0.001) Each construct was higher than the square root of AVE. This demonstrated that variables were closely connected and differed to each other at the same time. Therefore, the discriminant validity of our scale was satisfying (37).

Table 2

Convergent validity test.
Variables			Factor loading	CR	AVE
Attitude	←	A1	0.898	0.877	0.708
	←	A2	0.931
	←	A3	0.671
SN	←	SN1	0.742	0.877	0.704
	←	SN2	0.906
	←	SN3	0.861
PBC	←	PBC1	0.887	0.856	0.601
	←	PBC2	0.797
	←	PBC3	0.776
	←	PBC4	0.615
BI	←	BI1	0.813	0.894	0.734
	←	BI2	0.93
	←	BI3	0.831
Note: A, Attitude; SN, subjective norm; PBC, perceived behavior control; BI,behavior intention.

Table 3

Discriminant validity test.
Variable	Attitude	SN	PBC	BI
Attitude	0.842
SN	0.587 ***	0.766
PBC	0.293 ***	0.397 ***	0.63
BI	0.498 ***	0.553 ***	0.343 ***	0.586
Note: SN, subjective norm; PBC, perceived behavioral control; BI, behavior intention; Diagonals (in bold) represent the square root of the AVE.

3.4 Test of structural equation model

Knowledge and past behavior were added to the original model. Amos software was used to conduct goodness-of-fit test and path analysis on the prediction model of breast cancer screening behavior of primary physicians. Each goodness-of-fit test index is shown in Table 4, indicating that the overall model was well fitted. The standardized coefficient paths among the latent variables in the structural equation model are shown in Fig. 2, and the results of hypothesis testing are shown in Table 5. The relationship among the variables in the structural equation model are all at a significant level (P < 0.001). Thus, all the ten hypotheses above are valid, and all variables and their related ones can be explained. As is shown in our results, attitude towards breast cancer (β = 0.251, P < 0.001) and perceived norms of behavior (β = 0.140, P < 0.001) were significantly correlated with primary physicians’ intention to provide BCSs. SN had a high degree of connection with attitudes of primary medical staff (β = 0.573, P < 0.001), and moderately correlated with PBC (β = 0.258, P < 0.001) and behavioral intention (β = 0.352, P < 0.001). In addition, according to the results, the level of knowledge about breast cancer screening significantly affected PBC (β = 0.204, P < 0.001) and attitude (β = 0.168, P < 0.001) of primary medical staff to provide BCS. However, past screening had moderate significant effects on SN (β = 0.326, P < 0.001) and PBC (β = 0.370, P < 0.001) and knowledge (β = 0.368, P < 0.001).

Table 5

Results of structural equation modeling analysis
The hypothesis (H)		S.E.	C.R.	Estimate	P	Supported
H1:	BI ← Attitude	0.031	0.004	0.251	***	Yes
H2:	BI←SN	0.053	0.006	0.352	***	Yes
H3:	BI←PBC	0.021	0.005	0.140	***	Yes
H4:	Attitude ← SN	0.704	0.038	0.573	***	Yes
H5:	PBC ← SN	0.258	0.031	0.258	***	Yes
H6:	PBC ← Knowledge	6.148	1.322	0.204	***	Yes
H7:	Attitude ← Knowledge	6.197	1.379	0.168	***	Yes
H8:	Knowledge ← PB	0.046	0.007	0.368	***	Yes
H9:	SN ← PB	1.233	0.125	0.326	***	Yes
H10:	PBC ← PB	1.399	0.138	0.37	***	Yes
Note: BI, behavior; SN, subjective norm; PBC, perceived behavior control; PB, Past behavior

Our research is the first known study to understand physicians’ breast cancer screening behavior based on TPB theory(38). The result showed that physicians generally held high intentions to practice BCSs for rural women. This may cause by the National Breast Cancer Screening Program in Rural Areas starting from 2009 in China, lots of physicians have been organized or trained since then. In accordance with the hypotheses based on the TPB, the result of the path analysis test ascertained that primary care physicians’ attitude, SN, PBC ,PB and knowledge level can all directly or indirectly affect their intention to perform the BCSs. Our model is improved by adding knowledge and past behavior. It not only enriches the application scope of TPB, but also provides a new idea for studying the behavioral intention of breast cancer screening among primary medical staff in China. We offer a comprehensive analysis of variables which affect primary physicians’ intention.

This study showed that SN has the most significant impact on the intention of primary physicians to provide BCSs, followed by attitudes and PBC. Meanwhile, the variable SN2 (support from visiting women) had the highest factor loading (0.82). This result differs from a previous (31) study which found SN has little effect on intention, this may cause by differences in working environments and social pressures. The significance of SN indicates that supports from important others is the main factor affecting the willingness of primary physicians to provide BCSs, especially those from participants in BCSs, Because the physician-patient relationship is the most fundamental unit of decision making, this interpersonal and interdependent relationship between physician and patient may help to understand the significant impact of SN on primary care physicians' intentions to provide breast cancer screening for rural women. Whereas research have pointed out that due to reasons such as region, culture, doctor-patient communication, some rural women in China hold negative view of breast cancer screening and primary physicians (39), participants in our study also scored relatively low in variable SN. This might further destroy the trust between physicians and their visitors and decrease their intention to provide breast cancer screening ultimately. Research has found that fear of physicians and hospitals as the main barriers for not practicing screening for breast cancer (40). To prevent this situation, hospitals should create a friendly atmosphere and strengthen physicians’ support from their colleagues and, especially, visitors. Training and lectures on doctor-patient communication should be carried out regularly in hospitals to improve their service quality. At the same time, regular publicity and education in the community and the distribution of brochures in the community should be carried out to enhance the understanding of the breast cancer screening program among women of appropriate age. Hospitals, governments, and districts should work together to create a friendly, supportive and atmosphere.

PBC was quite important in behavior prediction(38). We presume that lack of time, equipment and skill training are an obstacle for primary physicians to implement breast cancer screening for rural women. Other research were also demonstrated that physicians PBC affect their medical behavior(12, 41). This demonstrates that the more restrictions that primary medical staff perceive, the lower their willingness to be engaged in screening work. Inadequate equipment in hospital, heavy workload, low salary level and lack of skill practice all negatively affect their intention to provide breast cancer screening. For a long time, medical resources in rural areas are relatively short, which gave physicians more difficulties. Investigations have shown that only a few primary care institutions in China are capable of carrying out ultrasound, mammography, human papillomavirus detection and thin-layer cytology detection(4), a research conducted in Spain(42) also reported that workload and financial limitations were the Spanish rural physicians’ common barriers, so rural physicians in developing countries are more likely to be lack of relevant knowledge, skills and experience of BCSs. Primary care physicians in the present study also hold negative perception of behavioral control of engaging in BCSs Thus, more researches are needed to mitigate the uneven distribution of medical resources and more effective policies should be introduced to improve the situation. We suggest that government and primary medical institutions act according to three aspects: 1) providing regular training to enrich physicians’ skill of BCSs. 2) strengthen policy support for rural medical and health care physicians, formulate relevant policies to support continuing education and advanced studies for primary care physicians. 3) increase investment in relevant screening equipment in rural medical institutions. In this way, limitation can be reduced and quality of rural breast cancer screening can be improved.

Consistent with TPB, attitude has a positive impact on the behavioral intention of primary care physicians, which indicates that making physicians more aware of the importance of breast cancer screening and improving their satisfaction in providing breast cancer screening services can significantly improve their behavioral intention. Therefore, it is important for hospitals and governments to establish a favorable impression of BCSs’ utilization, which can be done by building an innovative and incentive atmosphere When conducting BCSs. Meanwhile, we suggest that successful cases and examples be presented to physicians and visitors to convince them that breast cancer screening is beneficial for women health. Via recommendation from physicians, women will be more willing to take part in BCS, and their feedback will provide health workers with confidence.

The present study figured out that knowledge of breast cancer screening also influences primary physicians’ intention indirectly. This finding is consistent with other researches (43, 44). As a result, methods which are beneficial for enriching physicians’ and participators’ knowledge should be adopted, primary hospitals may regularly conduct training, competitions, lectures and other activities to improve the physicians’ knowledge level of Breast cancer. Health institutions can also encourage physicians to introduce breast cancer screening to visitors during their daily outpatient services. Their description of benefits, policies and requirements of breast cancer screening may significantly increase women’s participating rate. Our study also added past screening behavior to our model. It has been confirmed that past behavior indirectly influences health workers’ intention (45). Our results also show that past behavior exerts a moderate significant impact on intention. Therefore, institutions can take advantage of this finding. For instance, primary physicians who have been participating in screening services may set up a special team to regularly publicize their experience. At the same time, more obstetrics and gynecology specialists and general practitioners can be involved in BCS. Currently, rural areas in China are lack of experienced health workers and are unable to attract high-quality resources (46). Various methods should also be adopted to improve physicians’ sense of responsibility.

Strengths and limitations

Our study has limitations. Firstly, this is a cross-sectional study. As a result, the causal relationship between variables needs to be further verified. Secondly, this research only investigated factors of behavioral intention of primary medical staff instead of investigating the specific screening behavior. Although there is a strong correlation between intention and behavior, there remains a need to investigate actual screening service conducted by primary physicians. Moreover, only primary care physicians were studied in this research, many other allied health care professionals can impact screening behaviors. it would be valuable to further study a more diverse cohort. Our study also has some strengths. Firstly, it’s based on typical TPB, and we add knowledge and past behavior to improve our model. Secondly, our sample size (n = 1,101) is large and covers 6 cities in Jiangsu Province. Therefore, our study is reliable of providing suggestions to distribute breast cancer screening and improve rural women's health.

Our study extended the TPB model by adding knowledge and past behavior to predict primary physicians’ intention to provide breast cancer screening for rural women in China. We find out that attitude, SN, PBC all exert impact on their intention, and these variables may be paid more attention to in order to improve rural women’s health situation in China. Meanwhile, interventions of BCSs knowledge and skill development and strategies such as regular training, competitions, lectures, and other activities can be adopted to promote primary care physicians’' engagement in breast cancer screening program.

Author Contributions

YH: Methodology, Supervision, Funding acquisition, Project administration, Resources, Writing- review and editing. ZH: Methodology, Software, Formal analysis, Writing-original draft preparation. HZ: Methodology, Software, Writing--original draft preparation. YW: Validation, Data curation. YS: Data curation, Investigation. RM: Validation, Data curation. KS: Visualization, Investigation. KX: Resources.

Funding

The study was funded by the National Natural Science Foundation of China (Grant No. 71804074), the National Natural Science Foundation of China (Grant No. 72174092) and the research of health instruction to chronic patients and its effects under major public health events- a specific program from the Research Center of Major Public Health Events, Nanjing Medical University.

Ethics Approval

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Sir Run Run Hospital, Nanjing Medical University. (protocol code is 2019-SR-017. The date of approval is August 19, 2019).” The informed consent information was presented on the first sheet of the questionnaire. Each participant completed the questionnaire after providing informed consent. All surveys were anonymous.

Conflict of Interests

The author reports no conflicts of interest in this work.

Ross RL, Rubio K, Rodriguez HP. Mammography and Decision Aid Use for Breast Cancer Screening in Older Women. American Journal of Preventive Medicine (2022) 63(4):630–5. doi: 10.1016/j.amepre.2022.04.014.
Chen WQ, Zheng RS, Baade PD, Zhang SW, Zeng HM, Bray F, et al. Cancer Statistics in China, 2015. Ca-a Cancer Journal for Clinicians (2016) 66(2):115–32. doi: 10.3322/caac.21338.
Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer Statistics in China, 2015. Ca-a Cancer Journal for Clinicians (2016) 66(2):115–32. doi: 10.3322/caac.21338.
Di J, Rutherford S, Chu C. Review of the Cervical Cancer Burden and Population-Based Cervical Cancer Screening in China. Asian Pacific journal of cancer prevention: APJCP (2015) 16(17):7401–7.
Ilevbare OE, Adegoke AA, Adelowo CM. Drivers of Cervical Cancer Screening Uptake in Ibadan, Nigeria. Heliyon (2020) 6(3). doi: 10.1016/j.heliyon.2020.e03505.
Wang X, Li YJ, Lei L, Wu YJ, Zhao FH, Shi JF. [Access to Breast Cancer Screening among Females in China: A Focus Report on Screening Rate and Composition]. Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi (2023) 44(8):1302-8. Epub 2023/09/04. doi: 10.3760/cma.j.cn112338-20230103-00001.
Sun Y, Yuan J, Liu W, Qin B, Hu Z, Li J, et al. Predicting Rural Women's Breast Cancer Screening Intention in China: A Pls-Sem Approach Based on the Theory of Planned Behavior. Frontiers in public health (2022) 10:858788. Epub 2022/04/29. doi: 10.3389/fpubh.2022.858788.
Weinberger M, Saunders AF, Samsa GP, Bearon LB, Gold DT, Brown JT, et al. Breast Cancer Screening in Older Women: Practices and Barriers Reported by Primary Care Physicians. Journal of the American Geriatrics Society (1991) 39(1):22–9. doi: 10.1111/j.1532-5415.1991.tb05901.x.
Jia M, Zheng R, Zhang S, Zeng H, Zou X, Chen W. Female Breast Cancer Incidence and Mortality in 2011, China. Journal of Thoracic Disease (2015) 7(7):1221–6. doi: 10.3978/j.issn.2072-1439.2015.05.15.
Gu C, Chan CWH, Twinn S, Choi KC. The Influence of Knowledge and Perception of the Risk of Cervical Cancer on Screening Behavior in Mainland Chinese Women. Psycho-Oncology (2012) 21(12):1299–308. doi: 10.1002/pon.2037.
Kwok C, White K, Roydhouse JK. Chinese-Australian Women's Knowledge, Facilitators and Barriers Related to Cervical Cancer Screening: A Qualitative Study. Journal of immigrant and minority health (2011) 13(6):1076–83. doi: 10.1007/s10903-011-9491-4.
Heena H, Durrani S, Riaz M, AlFayyad I, Tabasim R, Parvez G, et al. Knowledge, Attitudes, and Practices Related to Breast Cancer Screening among Female Health Care Professionals: A Cross Sectional Study. Bmc Womens Health (2019) 19(1). doi: 10.1186/s12905-019-0819-x.
Grady KE, Lemkau JP, McVay JM, Reisine ST. The Importance of Physician Encouragement in Breast Cancer Screening of Older Women. Preventive medicine (1992) 21(6):766–80. doi: 10.1016/0091-7435(92)90083-t.
Nazari M, Ghazaani FM, Kaveh MH, Karimi M, Ghahremani L. Investigating the Predictors of Breast Cancer Screening Behaviors (Breast Self-Examination, Clinical Examination or Examination by Physician/Midwife and Mammography) Based on Protection Motivation Theory (Pmt) in Women. Journal of preventive medicine and hygiene (2021) 62(3):E736-e41. Epub 2021/12/16. doi: 10.15167/2421-4248/jpmh2021.62.3.1857.
Heena H, Durrani S, Riaz M, AlFayyad I, Tabasim R, Parvez G, et al. Knowledge, Attitudes, and Practices Related to Breast Cancer Screening among Female Health Care Professionals: A Cross Sectional Study. BMC women's health (2019) 19(1):122. Epub 2019/10/24. doi: 10.1186/s12905-019-0819-x.
Ajzen I. The Theory of Planned Behavior. Organ Behav Hum Decis Process (1991) 50:179–211. doi: 10.1016/0749-5978(91)90020-T.
Alradini F, Bepari A, AlNasser BH, AlGheshem EF, AlGhamdi WK. Perceptions of Primary Health Care Physicians About the Prescription of Antibiotics in Saudi Arabia: Based on the Model of Theory of Planned Behaviour. Saudi Pharmaceutical Journal (2021) 29(12):1416–25. doi: 10.1016/j.jsps.2021.10.011.
Liu C, Liu C, Wang D, Deng Z, Tang Y, Zhang X. Determinants of Antibiotic Prescribing Behaviors of Primary Care Physicians in Hubei of China: A Structural Equation Model Based on the Theory of Planned Behavior. Antimicrobial Resistance and Infection Control (2019) 8. doi: 10.1186/s13756-019-0478-6.
Galaviz KI, Jauregui-Ulloa E, Fabrigar LR, Latimer-Cheung A, Lopez y Taylor J, Levesque L. Physical Activity Prescription among Mexican Physicians: A Structural Equation Analysis of the Theory of Planned Behaviour. International Journal of Clinical Practice (2015) 69(3):375–83. doi: 10.1111/ijcp.12546.
Herbert KE, Urmie JM, Newland BA, Farris KB. Prediction of Pharmacist Intention to Provide Medicare Medication Therapy Management Services Using the Theory of Planned Behavior. Research in social & administrative pharmacy: RSAP (2006) 2(3):299–314. doi: 10.1016/j.sapharm.2006.02.008.
Via-Clavero G, Guardia-Olmos J, Gallart-Vive E, Arias-Rivera S, Castanera-Duro A, Delgado-Hito P. Development and Initial Validation of a Theory of Planned Behaviour Questionnaire to Assess Critical Care Nurses' Intention to Use Physical Restraints. Journal of Advanced Nursing (2019) 75(9):2036–49. doi: 10.1111/jan.14046.
Ajzen I. Constructing a Tpb Questionnaire: Conceptual and Methodological Considerations. Accessed 28/12/2007. (2002).
Allenbaugh J, Corbelli J, Rack L, Rubio D, Spagnoletti C. A Brief Communication Curriculum Improves Resident and Nurse Communication Skills and Patient Satisfaction. Journal of general internal medicine (2019) 34(7):1167–73. Epub 2019/04/19. doi: 10.1007/s11606-019-04951-6.
Kiyang L-N, Labrecque M, Doualla-Bell F, Turcotte S, Farley C, Cionti Bas M, et al. Family Physicians' Intention to Support Women in Making Informed Decisions About Breast Cancer Screening with Mammography: A Cross-Sectional Survey. BMC research notes (2015) 8:663-. doi: 10.1186/s13104-015-1608-8.
Tama RAZ, Ying L, Yu M, Hoque MM, Adnan KMM, Sarker SA. Assessing Farmers' Intention Towards Conservation Agriculture by Using the Extended Theory of Planned Behavior. Journal of Environmental Management (2021) 280. doi: 10.1016/j.jenvman.2020.111654.
Polonsky MJ, Renzaho AMN, Ferdous AS, McQuilten Z. African Culturally and Linguistically Diverse Communities' Blood Donation Intentions in Australia: Integrating Knowledge into the Theory of Planned Behavior. Transfusion (2013) 53(7):1475–86. doi: 10.1111/j.1537-2995.2012.03915.x.
Li J, Hu H, Liu W, Lei CI, Ung COL. Predicting Pharmacist Intention to Contribute to Covid-19 Management at the Community Level: A Cross-Sectional Survey Study. Frontiers in Public Health (2021) 9. doi: 10.3389/fpubh.2021.653335.
He Y, Yang F, Mu D, Xing Y, Li X. Examination of Psychosocial Predictors of Chinese Hospital Pharmacists' Intention to Provide Clinical Pharmacy Services Using the Theory of Planned Behaviour: A Cross-Sectional Questionnaire Study. BMJ open (2016) 6(10):e012775. Epub 2016/10/07. doi: 10.1136/bmjopen-2016-012775.
Brickell TA, Chatzisarantis NL, Pretty GM. Using Past Behaviour and Spontaneous Implementation Intentions to Enhance the Utility of the Theory of Planned Behaviour in Predicting Exercise. British journal of health psychology (2006) 11(Pt 2):249–62. Epub 2006/04/29. doi: 10.1348/135910705x52471.
Sur MH, Jung J, Shapiro DR. Theory of Planned Behavior to Promote Physical Activity of Adults with Physical Disabilities: Meta-Analytic Structural Equation Modeling. Disability and health journal (2022) 15(1):101199. Epub 2021/08/31. doi: 10.1016/j.dhjo.2021.101199.
Plotnikoff RC, Lippke S, Courneya K, Birkett N, Sigal R. Physical Activity and Diabetes: An Application of the Theory of Planned Behaviour to Explain Physical Activity for Type 1 and Type 2 Diabetes in an Adult Population Sample. Psychology & health (2010) 25(1):7–23. doi: 10.1080/08870440802160984.
Weber A, Dwyer T, Mummery K. Morphine Administration by Paramedics: An Application of the Theory of Planned Behaviour. Injury (2012) 43(9):1393–6. Epub 2011/01/11. doi: 10.1016/j.injury.2010.12.006.
Kaiser HF. Little Jiffy, Mark Iv. Educational and Psychological Measurement (1974) 34(1). doi: 10.1177/001316447403400115.
Hu L-t, Bentler PM. Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling-a Multidisciplinary Journal (1999) 6(1):1–55. doi: 10.1080/10705519909540118.
Fornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research (1981) 18(1). doi: 10.1177/002224378101800312.
Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis: A Global Perspective: Multivariate Data Analysis: A Global Perspective (2010).
Gefen D, Straub DW, Boudreau M. Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems (2000) 4(7).
Shi H, Wang J, Huang R, Zhao J, Zhang Y, Jiang N, et al. Application of the Extended Theory of Planned Behavior to Understand Chinese Students' Intention to Improve Their Oral Health Behaviors: A Cross-Sectional Study. Bmc Public Health (2021) 21(1). doi: 10.1186/s12889-021-12329-9.
Gu C, Chan CWH, Chow KM, Yang S, Luo Y, Cheng H, et al. Understanding the Cervical Screening Behaviour of Chinese Women: The Role of Health Care System and Health Professions. Applied Nursing Research (2018) 39:58–64. doi: 10.1016/j.apnr.2017.09.009.
Abdel-Aziz SB, Amin TT, Al-Gadeeb MB, Alhassar AI, Al-Ramadan A, Al-Helal M, et al. Perceived Barriers to Breast Cancer Screening among Saudi Women at Primary Care Setting. Journal of preventive medicine and hygiene (2018) 59(1):E20-e9. Epub 2018/06/26. doi: 10.15167/2421-4248/jpmh2018.59.1.689.
Hsieh PJ. Physicians' Acceptance of Electronic Medical Records Exchange: An Extension of the Decomposed Tpb Model with Institutional Trust and Perceived Risk. International journal of medical informatics (2015) 84(1):1–14. Epub 2014/09/23. doi: 10.1016/j.ijmedinf.2014.08.008.
Laza-Vásquez C, Hernández-Leal MJ, Carles-Lavila M, Pérez-Lacasta MJ, Cruz-Esteve I, Rué M, et al. Barriers and Facilitators to the Implementation of a Personalized Breast Cancer Screening Program: Views of Spanish Health Professionals. International journal of environmental research and public health (2022) 19(3). Epub 2022/02/16. doi: 10.3390/ijerph19031406.
Leilah Z, Emma S, Stuart M, Emily G, Oguchi N. Knowledge, Attitudes and Practices Regarding Cervical Cancer and Screening among Haitian Health Care Workers. International Journal of Environmental Research & Public Health (2014) 11(11):11541–52. doi: 10.3390/ijerph111111541.
Werner P, Karnieli E. A Model of the Willingness to Use Telemedicine for Routine and Specialized Care. Journal of telemedicine and telecare (2003) 9(5):264–72. Epub 2003/11/06. doi: 10.1258/135763303769211274.
Shiryazdi SM, Kholasehzadeh G, Neamatzadeh H, Kargar S. Health Beliefs and Breast Cancer Screening Behaviors among Iranian Female Health Workers. Asian Pac J Cancer Prev (2014) 15(22):9817–22. Epub 2014/12/19. doi: 10.7314/apjcp.2014.15.22.9817.
Zhang Y, Coyte PC. Inequality of Opportunity in Healthcare Expenditures: Evidence from China. BMC health services research (2020) 20(1):379. Epub 2020/05/08. doi: 10.1186/s12913-020-05252-z.

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Determinants of primary care physicians’ intention to provide Breast cancer screening services for rural women: a structural equation model based on the theory of planned behavior

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Abstract

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1 Introduction

2 Materials and Methods

2.1 Participants and data collection

2.2 Questionnaire

2.2.1 Attitude Subscale

2.2.2 Subjective norm Subscale

2.2.3 Perceived behavioral control Subscale

2.2.4 Behavior intention Subscale

2.2.5 Knowledge Subscale

2.2.6 Past Behavior Subscale

2.3 Data and Statistical Analysis

3 Results

3.1 Sample characteristics

3.2 Descriptive analysis

3.3 Instrument reliability and validity

3.4 Test of structural equation model

4 Discussion

5 Conclusions

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References

Tables

Additional Declarations

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