Parental health belief and behavioral factors influencing oral health behaviors of preschoolers in shanghai: a cross-sectional study

Abstract

Children’s oral health has been a significant public health issue. This study examined the impact of parental health beliefs and behaviors on preschoolers’ oral health behaviors (OHBs) and identified key predictors to guide future interventions. Conducted among 2,393 parents from 46 kindergartens in Minhang District, Shanghai, China, a cross-sectional study collected data via an online questionnaire covering demographics, children’s and parental OHBs, parental oral health knowledge, and health beliefs based on the Health Belief Model (HBM). Exploratory factor analysis and hierarchical multiple regression were employed to analyze HBM items and examine relationships between these factors and children’s OHBs. The results revealed that the model explained 44.4% of the variance in children’s OHBs. Significant predictors included child’s age, grade level, parental OHBs, parental oral health knowledge, perceived barriers, and cues to action. Parental OHBs were the strongest predictor, explaining 37.6% of the variance. The study highlights the importance of enhancing parental OHBs and knowledge, reducing perceived barriers, and increasing cues to action in interventions. Future interactive parent workshops in schools and communities should be implemented to strengthen children’s oral care skill training, specifically targeting parental cognitive misconceptions while providing accessible oral health resources and conditions.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-13422-4.

Introduction

Preschool children (usually 3–6 years old) are in a critical period for developing oral health and eating habits. Establishing good routines during this time not only promotes healthy milk teeth and proper chewing, but also sets the foundation for the development of permanent teeth and lifelong dental health. Nowadays, however, early childhood caries (ECC) has become the most common chronic disease among preschoolers¹. The 2017 Global Burden of Disease (GBD) study reported that approximately 530 million children have untreated caries in their milk teeth, and the caries rate among 5-year-olds in China is up to more than 70%^2,3. Neglecting oral health in preschoolers often leads to chronic pain, discomfort from untreated caries, sleep loss, missed school days, and increased restricted activity^4,5. Severe ECC can even affect nutritional intake and growth⁵. Moreover, children with ECC are more likely to experience further dental caries throughout childhood⁶.

Studies have shown that early health behavioral factors, such as adopting appropriate oral health behaviors (OHBs): proper and regular brushing, flossing, rinsing after meals or sweets, and regular oral checkups, can play a major role in the incidence of oral diseases, especially ECC^7–10. However, survey from China have shown that less than 20% of preschoolers have had an oral examination in the past year¹¹. Additionally, only 50.9% of these children brush their teeth twice daily, while a mere 9.5% floss twice a day¹²leaving an urgent need for improvement in their OHBs.

Preschoolers’ OHBs are largely influenced by their family environment due to their cognitive and behavioral immaturity¹³with parents or primary caregivers playing a crucial role^14–16. Based on social learning theory, indirect experience is acquired by observing the behavior of the role model, and the outward behavior of significant others is an important source of social influence¹⁷. Consistent behavior patterns in childhood are established at home, with parents serving as key role models for health behaviors^18–20. Evidence suggests that parental behaviors are strongly linked to children’s behaviors, particularly in areas such as smoking, alcohol consumption, and dietary habits²¹. Similarly, parents who practice good oral health report more positive oral health behaviors in their children²².

Parental oral health knowledge is another key factor influencing children’s OHBs. While some studies have shown that increased parental knowledge is associated with better child behavioral development⁶other studies have found that high levels of parent-reported knowledge are paradoxically associated with higher rates of childhood caries²³. To explore these complex relationships, the Health Belief Model (HBM) provides a valuable framework for understanding how parental beliefs and perceptions influence their behavior in preventing ECC²⁴. According to HBM, an individual’s engagement in health-related behaviors can be explained by perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and cues to action²⁵. The theory posits that individuals are more likely to adopt or help others adopt healthy behaviors when they perceive risks and believe that these behaviors promote their own health or that of others²⁶. HBM has been widely used for a variety of health behaviors, including OHBs in preschoolers¹⁵parents’ subjective perceptions of their child’s caries susceptibility and severity, as well as behavioral evaluations of the benefits of and barriers to OHBs, may directly influence the adoption of preventive and management behaviors for their child’s oral cavity^27–30.

Although related studies exist, the effects of the above-mentioned parental/caregiver factors on preschoolers’ OHBs have not been fully explored in the Chinese context. In China, systemic factors such as fragmented access to pediatric dental care, reliance on multigenerational caregiving, and a school-based health education system may uniquely shape parental health beliefs and practices. Furthermore, cultural norms emphasizing symptom-driven rather than preventive healthcare utilization could exacerbate parental perceived barriers to adopting regular OHBs. Therefore, by centering on parental/caregiver influences through the HBM framework, this study investigated the determinants of preschoolers’ OHBs, intending to inform the development of culturally appropriate and effective interventions aimed at reducing caries rates among preschoolers.

Methods

Setting and Participants

This study employed the cluster sampling and recruited participants being parents of preschool children from 46 out of 180 kindergartens in Minhang District, Shanghai, where a total of 54,233 children are enrolled. Inclusion criteria included parents (1) who were guardians of the children, (2) whose child(ren) aged 3–6 years, and (3) who were able to complete the questionnaire independently. Exclusion criteria included parents (1) who had participated in other oral health education programs in the past 12 months; and (2) whose child(ren) with severe oral or mandibular trauma. The study created a web-based questionnaire using the online survey platform Questionnaire Star (https://www.wjx.cn/). On December 4, 2023, the questionnaire was distributed to each kindergarten’s group via WeChat, a widely used social media platform in China. Kindergarten teachers shared the questionnaire link with 4,528 parents in their respective WeChat groups and provided a brief explanation of the study’s purpose. By March 7, 2024, a total of 3,169 questionnaires were received, yielding a 70% response rate. Three quality control questions were included to ensure response accuracy, and 2,393 valid questionnaires were retained after screening. The survey required participants to complete all questions before submission, and each account was limited to a single submission. Prior to participation, parents received an electronic informed consent form outlining the study’s purpose and significance of the study, informing them that they would be providing data for scientific research. It assured participants that all data were confidential and anonymous, emphasizing that the information would be used only for research purposes. Parents had the right to decide whether to participate in the survey.

The study obtained informed consent from all subjects and/or their legal guardian(s) and their answers to the questionnaire. The study was approved by the Ethics Committee of Minhang District Central Hospital (Pre-approval number: K2024-062). All methods were conducted under relevant guidelines and regulations.

Measurement

Data were collected using a structured questionnaire that included questions about sociodemographic characteristics (child age, gender, grade, parent age, gender, education level), family status (number of children in the household, family structure), parent’s oral health knowledge, behaviors, health beliefs, and child’s OHBs.

Child oral health behaviors (OHBs)

Referring to the 4th National Oral Health Epidemiology Questionnaire³¹we measured preschooler’s OHBs using a self-developed questionnaire with eight items. Measuring OHBs in terms of brushing, flossing, and rinsing after meals and sweets, such as “How many times does your child brush his/her teeth every day?”, “How long does your child brush his/her teeth each time?”, “How often does your child usually rinse his/her mouth after meals?” The items were reweighted based on the previous literature and the results of discussions within the research team. Each item was assigned a score from 0 to 1 based on the responses, with sugary snack consumption reverse-coded. The OHBs score was the sum of the eight-item scores (ranging from 0 to 8), with higher scores reflecting better levels of OHBs among preschoolers. The full questionnaire is provided in supplementary materials. In this study, the questionnaire had a Cronbach alpha coefficient of 0.64.

Parental oral health behaviors (OHBs)

Referring to the 4th National Oral Health Epidemiology Questionnaire³¹we measured parental OHBs using a self-developed questionnaire with seven items. Measuring OHBs in terms of brushing, flossing, and rinsing after meals and sweets, such as “How many times do you brush your teeth every day?”, “How long do you brush your teeth each time?”, and “How often do you usually rinse your mouth after meals?”. The items were reweighted based on the previous literature and the results of discussions within the research team. Each item was assigned a score from 0 to 1 based on the responses, with sugary snack consumption reverse-coded. The OHBs score was the sum of the seven-item scores (ranging from 0 to 7), with higher scores reflecting better levels of OHBs among parents of preschoolers. The full questionnaire is provided in supplementary materials. In this study, the questionnaire had a Cronbach alpha coefficient of 0.61.

Parental oral health knowledge

Based on previous studies, we measured parental knowledge about the oral health of preschool children using a self-developed questionnaire with eight entries. Oral health knowledge was measured regarding caries etiology, disease consequences, and prevention measures, such as “Bacteria that cause tooth decay can be spread from parent to child through sharing utensils,” “Oral disease affects general health,” “Both milk and permanent teeth need to be flossed”. Participants rated the questionnaire according to a 5-point Likert response format (1 = “strongly disagree” to 5 = “strongly agree”), with higher scores reflecting better parental knowledge of preschoolers’ oral health. In this study, the questionnaire had a Cronbach alpha coefficient of 0.83.

Parental health belief model (HBM)

To measure parental health beliefs about their children’s oral behavior, the HBM scale developed by the research team with reference to the questionnaire framework in the relevant literature was used in this study. Some modifications were made to the behavioral content of the questionnaire to better fit the purpose of this study, while the framework for each dimension was retained. The 20-item HBM scale was adapted from several studies on parental attitudes toward children’s behaviors^25,30,32,33covering five dimensions perceived susceptibility (2 items), perceived severity (4 items), perceived benefits (5 items), perceived barriers (6 items), and cues to action (4 items). For example, perceived severity and perceived barriers were measured using items such as “It would be serious if my child(ren) [disease consequences]”, “It is difficult for me to help my child(ren) [OHBs]”. During scale validation, exploratory factor analysis (EFA) was conducted with Bartlett’s test of sphericity and Kaiser-Meyer-Olkin measure to confirm suitability. Two items on perceived susceptibility were subsequently removed (< 0.5 factor loadings or > 0.4 cross-loadings), resulting in an 18-item scale retaining four dimensions for final analysis.

Participants were asked to rate all HBM items on a 5-point Likert scale (“1 = Strongly Disagree” to “5 = Strongly Agree”), and each dimension was expressed using a mean score, which was determined by summing and dividing the scores for each item under each dimension by the corresponding item’s score. Each dimension is shown as a mean score, calculated by adding the scores for all items in each dimension and dividing by the number of items. Higher scores indicate higher levels of the dimension. The Cronbach’s alpha coefficient for the total questionnaire was 0.89, while those for the subscales ranged from 0.81 to 0.88, with good internal consistency.

Statistical methods

Sociodemographic characteristics, as well as levels of parental oral health knowledge, health beliefs, and both parental and child oral health behaviors (OHBs), were analyzed descriptively. Mean ± SD were calculated for continuous variables. Numbers and percentages were calculated for categorical variables. Shapiro-Wilk was tested for normality. For comparisons of child OHBs across baseline characteristics, F-tests were applied to multicategorical variables, while t-tests were used for dichotomous variables. Pearson correlation analyses were performed to explore associations among the seven variables (parental OHBs, parental oral health knowledge, perceived severity, perceived benefits, perceived barriers, cues to action, and child OHBs). Hierarchical multiple regression analysis was used to examine the effect of the independent variables on child OHBs, and variables that were significant in the univariate analysis were included as covariates. Variance inflation factor (VIF) and tolerance were utilized to test for multicollinearity. Neither tolerance nor VIF values violated the assumption of multicollinearity (tolerance values > 0.1 and VIF values < 10).

All statistical analyses were performed using R version 4.3.3 and SPSS version 25.0. The two-sided statistical significance level was 0.05.

Results

Exploratory factor analysis, descriptive analysis and reliability of the HBM variables

Exploratory factor analysis (EFA) was conducted to validate the structure of the HBM scale within our study population. The Bartlett test of sphericity (χ2 = 23460.765, P < 0.001) and the Kaiser-Meyer-Olkin measure (0.88) validated the explainability of the exploratory factor analysis. Four factors with eigenvalues > 1 were retained, explaining 67.71% of the total variance. Two items measuring perceived susceptibility—“My child is at a high risk for developing oral diseases such as dental caries” and “If my child does not use proper brushing, he/she is at increased risk for oral diseases such as dental caries”—were eliminated due to low factor loadings (< 0.5) or high cross-loadings (> 0.4 on two or more factors). The remaining items had factor loadings ranging from 0.652 to 0.864, which is considered acceptable (see Table 1). The Cronbach’s alpha coefficients for the retained dimensions were 0.88 (perceived severity), 0.87 (perceived benefits), 0.87 (perceived barriers), and 0.81 (cues to action), indicating good internal consistency. The mean scores and standard deviations of the HBM variables are detailed in Table 1.

Table 1.

Exploratory factor analysis, descriptive analysis and reliability of the HBM variables.

Study variable	Item	Mean	SD	Item loading	α
Perceived severity		4.14	0.63		0.88
It would be serious for me if…
	my child developed cavities.	3.97	0.83	0.779
	my child had unattractive teeth.	4.07	0.77	0.833
	my child’s school and daily life were affected by oral disease.	4.23	0.69	0.854
	my child’s health was jeopardized by oral disease.	4.31	0.67	0.789
Perceived benefits		4.37	0.50		0.874
I think…is/are good for my child’s oral health.
	adopting proper brushing habits (brushing style, time, frequency, etc.)	4.50	0.53	0.803
	rinsing after meals	4.43	0.55	0.841
	daily lunchtime brushing	4.19	0.69	0.683
	regular dental checkups	4.37	0.59	0.763
Perceived barriers		2.60	0.79		0.868
It is difficult for me to help/supervise my child(ren) to…
	brush his/her(their) teeth the right way and for enough time.	2.88	1.09	0.804
	rinse his/her(their) mouths after meals.	2.70	1.01	0.864
	floss daily.	2.99	1.06	0.765
	prepare the dental tools needed during the school day.	2.09	0.98	0.676
	brush his/her(their) teeth at holiday lunchtime.	2.60	1.03	0.781
	take regular dental checkups.	2.34	0.95	0.715
Cues to action		4.02	0.60		0.807
I have learned knowledge about oral health problems in preschool children…
	in paper media or online media.	3.92	0.77	0.801
	from my relatives, colleagues, friends, etc.	3.68	0.87	0.822
	from the kindergarten.	4.23	0.67	0.677
	the doctor’s advice.	4.23	0.71	0.652

Preliminary analyses of demographic characteristics

Table 2 summarizes the sociodemographic characteristics among participants and the OHBs characteristics of their children. A total of 2,393 parents of preschoolers were included in this study, and most of their children (59.76%) were enrolled in the senior kindergarten grade. The vast majority (99.71%) were parents of children, more than half (68.78%) were female caregivers, and only a few participants (23.86%) did not have a bachelor’s degree or higher education. Of these participants’ families, more than half were raising only one child (56.08%), nearly half were raising two children (41.66%), remaining minority were raising three and more children (2.26%); 48.02% of these families were nuclear families and 49.31% were multigenerational families. There were significant differences in the level of child OHBs among participants with different kindergarten grades (F = 19.34, P < 0.001), number of children in the household (F = 4.81, P = 0.008), and family structure (F = 2.95, P = 0.019).

Table 2.

Descriptive statistics and the distribution of children’s oral health behavior.

Characteristics		N (%) or mean (SD)	Mean (SD) of Children’s oral health behavior	t/F	P
Child age (years)		5.44 (1.37)
Kindergarten grade				19.34	< 0.001
Toddler class		19 (0.79)	2.73 (0.81)
Primary class		287 (11.99)	3.11 (0.73)
Junior class		657 (27.46)	3.34 (0.82)
Senior class		1430 (59.76)	3.45 (0.78)
Children’s sex				-1.87	0.061
Male		1271 (53.11)	3.34 (0.79)
Female		1122 (46.89)	3.40 (0.81)
Parent age (years)		36.4 (4.39)
Parent sex				-1.85	0.064
Male		747 (31.22)	3.33 (0.83)
Female		1646 (68.78)	3.39 (0.78)
Parent education				0.30	0.767
College or below		571 (23.86)	3.38 (0.77)
Undergraduate or above		1822 (76.14)	3.37 (0.81)
Parent role				0.07	0.941
Parents		2386 (99.71)	3.37 (0.80)
Grandparents or Others		7 (0.29)	3.35 (0.51)
Number of Children in the household				4.81	0.008
1		1342 (56.08)	3.41 (0.80)
2		997 (41.66)	3.31 (0.78)
≥ 3		54 (2.26)	3.43 (0.84)
Family Structure				2.95	0.019
Nuclear family		1149 (48.02)	3.42 (0.82)
Multigenerational family		1180 (49.31)	3.33 (0.77)
Single-parent family		23 (0.96)	3.14 (0.91)
Skipped-generation/Grandparent family		39 (1.63)	3.23 (0.91)
Other types		2 (0.08)	3.02 (0.53)

Correlations between the variables

Table 3 shows the inter-variable associations among parental OHBs, parental oral health knowledge, each dimension of the HBM, and child OHBs. All variables show significant correlations, with the strongest positive correlation observed between parental oral health knowledge and perceived benefits (r = 0.638, P < 0.001). Parental OHBs were also strongly positively correlated with child OHBs (r = 0.626, P < 0.001). Perceived barriers showed negative correlations with other variables, with the strongest negative correlation found with child OHBs (r = -0.327, P < 0.001).

Table 3.

Correlations between the variables (n = 2393) *P<0.05;**P < 0.01; ***P < 0.001.

	Parental OHBs	Parents’ oral health knowledge	Perceived Severity	Perceived Benefits	Perceived Barriers	Cues to Action	Child OHBs
Parental OHBs	1
Parental oral health knowledge	0.207***	1
Perceived Severity	0.125***	0.465***	1
Perceived Benefits	0.205***	0.638***	0.542***	1
Perceived Barriers	-0.289***	-0.195***	-0.114***	-0.247***	1
Cues to Action	0.188***	0.466***	0.367***	0.537***	-0.218***	1
Child OHBs	0.626***	0.239***	0.122***	0.215***	-0.327***	0.253***	1

Hierarchical multiple regression analysis

Standardized (β) regression coefficients for the three models are reported in Table 4. Hierarchical multiple regression analyses were used to explore the relationships between preschoolers’ sociodemographic variables, parental oral behaviors, knowledge, and parental perceived severity, perceived benefits, perceived barriers, and cues to action regarding their children’s oral health. To enhance clarity and focus on the main predictors of interest, only key independent variables (i.e., parental OHBs, knowledge, and health belief dimensions) are presented in the regression table. Control variables, including child age, kindergarten grade, number of children in the household, and family structure, were included in all models but are not shown in the Table 4. Full results are available upon request.

Table 4.

Hierarchical multiple regression analysis of the relationship between child OHBs and socio-demographic factors, parental ohbs, knowledge and 4 dimensions of HBM variables.

	Model 1		Model 2		Model 3
	β	P	β	P	β	P
Parental OHBs	0.983***	< 0.001	0.591***	< 0.001	0.881***	< 0.001
Parental oral health knowledge			0.125***	< 0.001	0.122***	< 0.001
Perceived Severity					-0.015	0.347
Perceived Benefits					-0.032	0.251
Perceived Barriers					-0.141***	< 0.001
Cues to Action					0.083***	< 0.001
R2	0.411		0.425		0.448
AdjustR2	0.408		0.423		0.444
ΔR2	0. 411		0.015		0.021
F	150.8***		146.8***		120.6***

*P < 0.05, **P < 0.01, ***P < 0.001.

In all these models, all the variation infation factor (VIF) values were below 10.

Control variables, including child age, kindergarten grade, number of children in the household, and family structure, were included in all models but are not shown. Full results are available upon request.

Multicollinearity analyses were performed on all independent variables with all VIF < 1.47. Model 1, which included parental OHBs and controlled for child age, kindergarten grade, number of children in the household, and family structure, explained 40.8% of the variance in child OHBs (F = 150.8, P < 0.001). Model 2 augmented this to 42.3% (F = 146.8, P < 0.001) with parental knowledge. Finally, Model 3 incorporating health belief dimensions achieved 44.4% variance explained (F = 120.6, P < 0.001). The final model identified seven significant predictors, most notably parental OHBs (β = 0.881, P < 0.001) which alone explained 37.6% of variance, underscoring their modeling role. Other significant predictors (P < 0.01) included child age (β = 0.056), kindergarten grade (junior class: β = 0.276; senior class: β = 0.349), parental knowledge (β = 0.122), perceived barriers (β = -0.141), and cues to action (β = 0.083). Although the final model achieved moderate explanatory power (R²=44.4%), it still has room for other unmeasured influences such as school environment or peer behavior.

Discussion

Univariate analysis showed that older children and those in higher grade levels had better OHBs, consistent with previous research^34–36. Additionally, children without siblings had higher OHBs scores, possibly due to more resources being available in smaller families³⁷. Children from nuclear families also exhibited better OHBs compared to those from multigenerational households, potentially due to more focused parental involvement in nuclear families.

The results of the regression analysis showed that parental OHBs was the strongest predictor of preschooler’s OHBs, aligning with the social learning theory that children learn by observing and imitating the behaviors of significant others, especially parents¹⁷. At the preschool stage, children primarily imitate behaviors and are highly responsive to external guidance³⁸. with parents serving as key role models²⁰. Thus, parental behaviors like brushing, flossing, and rinsing are often mimicked by children²².

Our study also found that parental oral health knowledge had a significant impact on children’s OHBs, supporting findings from previous studies³⁹. This shows that knowledge can translate into action, although the impact is not as strong as the actual parental behavior. Given previous studies, knowledgeable parents may be better equipped to guide and supervise children’s oral hygiene practices⁶; and may be more inclined to create an environment conducive to oral health for their children^40,41. In addition, it was found that there was a significant correlation between oral health knowledge and the various constructs of the HBM (e.g., perceived benefits, and perceived barriers)⁴²both of which jointly affect mothers’ behaviors in managing their young children’s oral health⁴³.

Parental health beliefs also play a role in shaping OHBs. Parental good OHBs reflect their commitment to oral health and are positively associated with their beliefs and attitudes toward their children’s oral health⁴⁴. Among the health belief dimensions, perceived barriers had a significant negative association with child OHBs(β = -0.141, P < 0.01), consistent with previous research⁴⁵. Higher perceived barriers, such as time constraints, lack of resources, and limited child cooperation, make it harder for parents to help their children adopt OHBs. These findings are supported by other studies, which highlight challenges such as lack of time and energy, especially for dual-career couples in China^46,47.

Cues to action emerged as a statistically significant predictor in the Health Belief Model (β = 0.083, P < 0.01), indicating that the more external reminders and information parents received, the better levels of OHBs in their children, consistent with previous research findings^48,49. The cues to action in this study originated from four sources: mass media, interpersonal networks, schools, and hospitals. While confirming the theoretical relevance of cue-based interventions, its small effect size limited practical impact, indicates they should likely be supplemental rather than primary intervention targets.

The findings showed that most parents were aware of their child’s possible caries risk and the benefits of adopting OHBs, but this did not increase the level of their child’s OHBs. One possible explanation is that various constructs of HBM do not operate in isolation, but rather interact in more complex ways. Previous research using HBM to explain parents’ adoption of healthy behaviors toward their children found that not all HBM constructs had a significant effect^24,25. The same conditions were found in this study (perceived severity, perceived benefit were not significant variables), and inconsistent with the results of other studies on parental behaviors regarding preschoolers’ oral health^27,30,45,50.

Limitation

Our study has some limitations. First, we collected data via an online survey, the quality of which may have been compromised, even though this study has used quality control questions to ensure the validity of the questionnaire. Second, the data were collected through parent/caregiver self-reports, which may introduce recall bias, social desirability bias or misreporting, etc. Thirdly, it needs to be acknowledged that the internal consistency of both children’s and parental OHBs scales, as indicated by Cronbach’s α values (0.64 and 0.61, respectively), fell below the conventional threshold of 0.70. It may lead to measurement errors, unreliable or inconsistent findings, and misinterpretation of the data. Future studies with test-retest reliability assessments and validity processes are needed. In addition, the external validity of the questionaries used in the study still needs to be confirmed by more observational studies and further experimental research. Since the impact of single behavior and both of their item weights determination requires long-term observation and evidence-based results. Fourth, being a cross-sectional study is difficult to establish a true causal relationship, further longitudinal studies will be needed later to investigate the pathways among variables. Finally, the participants were all from Shanghai, one of the most developed cities in China, this could jeopardize the representativeness of the study sample and limit generalizability. Cluster sampling was employed. However, this hierarchical structure was not accounted for in our statistical model, which may impact the validity of the findings. Plus, the lack of post-hoc tests limits our ability to specify which groups differed significantly in univariate analyses. These findings should therefore be interpreted with caution. In future research, we plan to adopt more appropriate methods to enhance the accuracy of the results, and studies in other areas are pending to measure these variables.

Conclusion

This study found that parents’ own OHBs, knowledge, perceived barriers, and cues to action can be prioritized intervention targets for oral health intervention practices for preschoolers. It is recommended not only to reduce the barriers for parents to help/supervise their children’s adoption of oral health behaviors, but also to increase parents’ own knowledge, thereby improving their own oral behaviors. Future interactive parent workshops in schools and communities should be implemented to strengthen children’s oral care skill training, specifically targeting parental cognitive misconceptions while providing accessible oral health resources and conditions.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

X.X.M designed the work. X.R.S wrote the main manuscript text and interpreted the data. X.Y.X and H.Y.S took responsibility for the data acquisition. Y.Y.L and Q.Q.W substantively revised the work. All authors reviewed the manuscript.

Data availability

Dataset generated during this study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.