<p>The Influencing Factors of Health-Seeking Preference and Community Health Service Utilization Among Patients in Primary Care Reform in Xiamen, China</p>
Abstract:Introduction: Patients often seek healthcare at general hospitals rather than at community healthcare centres (CHCs) which leads to inefficiency of health services. The primary healthcare reform developed by Xiamen has proven to break through the barriers of hierarchical diagnosis and treatment. The influencing factors of health-seeking behaviours of patients in the Xiamen reform, however, are unclear.Objective: This study aimed to assess patients' healthcare-seeking preferences and CHCs utilization, and ident… Show more
“…This is consistent with another study showing that students with higher monthly income show more favorable attitudes towards healthcare than those with lower income [18]. Research about primary care also found out that patients with higher household incomes are more willing to use community healthcare centers [17]. Income, as well as education and occupation, as important components of socioeconomic status, are all in uencing factors of health-seeking behavior [19,20].…”
Section: Discussionsupporting
confidence: 88%
“…Smoking is considered one of the health risk behaviors which are linked to physical and psychological problems. Since smoking exposes individuals to greater health problems, it should increase health-seeking behavior [17]. However, this study nds the opposite effect among nursing students.…”
Background
Though relevant education and clinical practice could promote health-seeking behavior, nurses and nursing students may not actively seek healthcare.
Methods
This was a cross-sectional study using an adaptation of the self-reported Health Behavior Questionnaire (HBQ) including sociocultural background, lifestyle, self-assessment of life, health care utilization, and health counseling. 199 valid samples were acquired by convenient sampling. Univariate analysis, Spearman rank correlation, Pearson correlation, and multivariate linear regression were used to analyze the data.
Results
Cultural background, living with family, employment, most items in lifestyle, and all items in perceived life status were correlated with health-seeking behavior. A multivariate linear regression verified the influence of alcohol consumption, financial situation, and work situation on the experience of health care utilization, as well as the influence of physical health and interpersonal relationship on the experience of health counseling.
Conclusions
Less alcohol consumption, better financial situation, and better work situation are positively correlated with health care utilization. Better physical health and sounder interpersonal relationships can improve health counseling. The effect of other factors needs further exploration. Cohort studies could be used to investigate the long-term change in health-seeking behavior.
“…This is consistent with another study showing that students with higher monthly income show more favorable attitudes towards healthcare than those with lower income [18]. Research about primary care also found out that patients with higher household incomes are more willing to use community healthcare centers [17]. Income, as well as education and occupation, as important components of socioeconomic status, are all in uencing factors of health-seeking behavior [19,20].…”
Section: Discussionsupporting
confidence: 88%
“…Smoking is considered one of the health risk behaviors which are linked to physical and psychological problems. Since smoking exposes individuals to greater health problems, it should increase health-seeking behavior [17]. However, this study nds the opposite effect among nursing students.…”
Background
Though relevant education and clinical practice could promote health-seeking behavior, nurses and nursing students may not actively seek healthcare.
Methods
This was a cross-sectional study using an adaptation of the self-reported Health Behavior Questionnaire (HBQ) including sociocultural background, lifestyle, self-assessment of life, health care utilization, and health counseling. 199 valid samples were acquired by convenient sampling. Univariate analysis, Spearman rank correlation, Pearson correlation, and multivariate linear regression were used to analyze the data.
Results
Cultural background, living with family, employment, most items in lifestyle, and all items in perceived life status were correlated with health-seeking behavior. A multivariate linear regression verified the influence of alcohol consumption, financial situation, and work situation on the experience of health care utilization, as well as the influence of physical health and interpersonal relationship on the experience of health counseling.
Conclusions
Less alcohol consumption, better financial situation, and better work situation are positively correlated with health care utilization. Better physical health and sounder interpersonal relationships can improve health counseling. The effect of other factors needs further exploration. Cohort studies could be used to investigate the long-term change in health-seeking behavior.
“…Another study by Ovbiagele et al (43) found that nearly 70% of 144 stroke survivors maintained regular exercise upon being discharged from hospital, which was similar to but slightly higher than our ndings. Despite differences in research design, cultural background, and economical level, lack of conventional management(44), not ideal health care seeking behavior (45) have been main barriers for adherence behavior, and it is likely to present an even greater in uence on Chinese community-dwelling stroke survivors' long-term outcomes over the next few decades.…”
Background: Regular physical activity and exercise (PAE) after stroke is essential, but the adherence to PAE and its influential factors is rarely studied and unclear in developed countries. The aim of this study was to investigate the status of PAE adherence, and to identify its influential factors among Chinese community-dwelling stroke survivors.Methods: A cross-sectional study was conducted, 208 stroke survivors were randomly selected from three communities (60.10% men, mean age 70.25 ± 9.08 years). Physical Activity and Exercise Adherence Scale (PAEAS) was used as a measure of compliance, knowledge, attitude, self-efficacy and social support were measured using relevant scales. Influential factors of PAE adherence and associations between those variables were explored using multiple linear regression and path analysis.Results: The mean adherence rate was moderate (62.0%), stroke survivors tended to be more adherent to do PAE than monitoring and seeking advices (70.30% > 53.50% > 48.30%). Regression results revealed that seven factors significantly associated with PAE adherence, including knowledge, attitude, self-efficacy, social support, self-care ability, community rehabilitation experience, and times since stroke (R2 = 75.10%, F = 45.593, p < 0.05). Furthermore, path analysis showed that knowledge had a significant indirect positive influence with self-efficacy as a mediator on adherence (β= 0.16, p < 0.05); while attitude and social support impacted both directly and indirectly on adherence with self-efficacy as the mediator; what’s more, self-efficacy was an important predictor that performed the strongest direct effect on community-dwelling stroke survivors’ PAE adherence (β= 0.44, p < 0.01 ). These four variables accounted for a total of 67.00% of the variance of PAE adherence among community-dwelling stroke survivors.Conclusions: Physical activity and exercise adherence of community stroke survivors is needed to be improved. Attitude and social support presented both direct and indirect impacts on PAE adherence through self-efficacy, while knowledge only exhibited an indirect influencing effect on adherence. Health care professionals should administer a more comprehensive and systematic assessment to seek to improve self-efficacy through the development of effective interventions for promoting adherence to PAE in this population.
“…Recent studies have evaluated the effects of public hospital reform by comparing the proportion of patients at each level, of both hospital pre-reform and post-reform, with large-scale statistical data, or by surveying the change of patient attitudes toward primary care use after the reform [30,31]. For instance, Wu et al, (2017) examined public views toward the choice of first-contact care in the hierarchical medical system of China [7].…”
Section: Reforms For Optimizing Hospital Utilization In China Since 2009mentioning
The effects of public hospital reforms on spatial and temporal patterns of health-seeking behavior have received little attention due to small sample sizes and low spatiotemporal resolution of survey data. Without such information, however, health planners might be unable to adjust interventions in a timely manner, and they devise less-effective interventions. Recently, massive electronic trip records have been widely used to infer people’s health-seeking trips. With health-seeking trips inferred from smart card data, this paper mainly answers two questions: (i) how do public hospital reforms affect the hospital choices of patients? (ii) What are the spatial differences of the effects of public hospital reforms? To achieve these goals, tertiary hospital preferences, hospital bypass, and the efficiency of the health-seeking behaviors of patients, before and after Beijing’s public hospital reform in 2017, were compared. The results demonstrate that the effects of this reform on the hospital choices of patients were spatially different. In subdistricts with (or near) hospitals, the reform exerted the opposite impact on tertiary hospital preference compared with core and periphery areas. However, the reform had no significant effect on the tertiary hospital preference and hospital bypass in subdistricts without (or far away from) hospitals. Regarding the efficiency of the health-seeking behaviors of patients, the reform positively affected patient travel time, time of stay at hospitals, and arrival time. This study presents a time-efficient method to evaluate the effects of the recent public hospital reform in Beijing on a fine scale.
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