Predictors of mothers’ preventive behaviors for children’s dental trauma: a cross-sectional study using the health belief model

Research plan

The current cross-sectional study was conducted from September 19, 2023, to March 2, 2024, in Kashan, Isfahan province. As the largest city in Isfahan with a population of 364,482, Kashan served as the study setting. The research population consisted of mothers of children aged 7 to 12 years, who were selected from local schools. The inclusion criteria for the study were as follows: 1. Mothers with children aged 7–12; 2. Mothers residing in the city of Kashan; 3. Mothers reading and writing proficiency for questionnaire completion; 4. Mothers fluent in Persian to ensure proper understanding of study materials; and finally, mothers who were willing to participate in the study. The exclusion criteria were as follows: 1. Mothers of children with minor disabilities or specific medical conditions that could influence the risk of dental trauma; 2. Mothers who did not live in the study area or were temporary residents; and 3. Mothers whose children had previously suffered severe dental trauma that might have impacted their preventive behaviors.

In this study, all methods were carried out in accordance with the Declaration of Helsinki and approved by the ethics committee of Kashan University of Medical Sciences, with the ethics code of IR.KAUMS.NUHEPM.REC.1402.032. All these methods followed relevant guidelines, literature review, and regulations approved by the Kashan University of Medical Sciences, Kashan, Iran. Written informed consent was obtained from all participants prior to taking part in this study.r

Sample size and sampling technique

This study was conducted with the aim of predicting mothers’ behavior to prevent dental trauma in children. The sample size was estimated based on the following formula.

In related studies, for example, Momeni et al. [24] reported that the score of mothers’ preventive performance of dental trauma was 3.38 ± 1.2. Therefore, if margin of error (d) = 0.1, SD = 1.2, and\(\,{{{{\rm{Z}}}}}^{2}{}_{1-\propto /2}=1.96\), according to the formula above, the sample size was obtained 554 people. Taking into account a drop-out rate of 20% and above, the sample size was considered 700 people, who were selected based on the multi-stage random cluster sampling method. To gather samples, we focused on mothers of children aged 7 to 12. Initially, we approached the Kashan General Education Department and segmented the Kashan into five geographical regions: north, south, east, west, and center. We treated each of the five regions as a separate cluster, resulting in a total of five clusters. Subsequently, we obtained a list of both girls’ and boys’ schools in each cluster from the Education Department. From each of these clusters, we randomly selected two schools—one for girls and one for boys—culminating in a total of 10 schools. It is important to note that 70 mothers were randomly selected from each school, resulting in a total of 700 completed questionnaires.

Data collection process

Data were collected in two shifts, morning and afternoon, from 10 schools using a researcher-developed questionnaire by the corresponding author. The corresponding author visited the schools and coordinated with the administrators to obtain the schedule of parent-teacher meetings. On the day of the meetings, after the session concluded, mothers present were asked to stay for 15 to 20 min to complete the questionnaires. Following the introduction of the study and its objectives, the researcher randomly distributed questionnaires among the mothers of children aged 7–12 years. The researcher’s presence at the parent-teacher meetings of each school continued until the sample size was reached. To minimize bias, the researcher read the questions aloud.

Instrumentation

The researcher-developed questionnaire, grounded in the HBM, comprised three sections. The first section gathered demographic information through 13 questions, which included the child’s gender and age, the mother’s age, the child’s educational level, the mother’s and father’s education and job, the number of children in the family, economic status, housing situation, marital status, and inquiries such as, “Have you previously received information about the prevention of dental trauma?”

The second section comprised nine knowledge-related questions derived from a literature review. The knowledge-based questions (e.g., definition of dental trauma, complications, prevention) were scored based on accuracy. For each item, a “Yes” response was awarded 3 points only if it matched the correct answer derived from the standard dental trauma literature. Incorrect “No” responses received 2 points, while “I do not know” (reflecting lack of knowledge) was assigned 1 point. This scoring system ensured higher scores reflected better knowledge, with total scores ranging from 9 to 27. The third section of the questionnaire addressed the constructs of the HBM. In this study, questions were formulated to assess perceived susceptibility, perceived severity, perceived barriers, perceived benefits, self-efficacy, and behavior.

The perceived susceptibility construct comprised four questions, one of which was, “ likely my child is at risk of breaking a tooth while playing with friends.” Responses were measured using a 5-point Likert scale (strongly agree, agree, have no opinion, disagree, and strongly disagree), with the highest score assigned to “strongly agree” (score of 5) and the lowest to “strongly disagree” (score of 1). The total score ranged from 4 to 20. The perceived severity construct consisted of five questions, one of which was, “If my child’s tooth breaks, she will have difficulty eating properly.” Responses were assessed using a 5-point Likert scale (strongly agree, agree, have no opinion, disagree, and strongly disagree), with “strongly agree” receiving the highest score (5) and “strongly disagree” the lowest (1). The total score could range from 5 to 25.

The perceived benefits construct comprised five questions, one of which was, “Wearing a mouth guard while children play can help prevent tooth fractures.” In contrast, the perceived barriers construct also included five questions, such as, “Using devices to prevent tooth breakage in children may be expensive for families.” The self-efficacy construct included five questions, one of which was, “I can encourage my children to wear mouthguards while exercising.” The Likert scale for the perceived benefits and self-efficacy constructs was administered separately, using a 5-point scale (strongly agree, agree, have no opinion, disagree, and strongly disagree), where “strongly agree” received the highest score of 5 points and “strongly disagree” received the lowest score of 1 point. For the construct of perceived barriers, the scores were applied in reverse, that is, the highest score was given to strongly disagree (score 5) and the lowest score was given to strongly agree (score 1). The score range of all three constructs was between 5 and 25.

Five questions were created to assess mothers’ preventive behaviors regarding children’s dental trauma. One example question was, “Have you ever advised your children not to push each other while playing?”. Responses were measured using a 4-point Likert scale (never, sometimes, often, and always), with the highest score of 4 points assigned to “always” and the lowest score of 1 point given to “never.” The total score ranged from 4 to 16.

Validity and reliability of the instrumentation

To create the questionnaire, initial library research was conducted on the topic [25,26,27]. Face-to-face interviews were held with 10 mothers of children aged 7–12 (who did not participate in subsequent phases of the study) to assess the qualitative face validity. They were asked about the difficulty level, appropriateness, and clarity of the questionnaire items. To evaluate the quantitative content validity of the tool, 10 experts in dentistry, health education, and nursing were consulted regarding the necessity, relevance, simplicity, and clarity of each question. Based on Lawshe’s criteria [28], for a panel of 10 experts, items with a Content Validity Ratio (CVR) exceeding 0.62 are deemed statistically significant. In the present study, the calculated CVR of 0.79 meets this threshold, confirming the essentiality of the included items. For the Content Validity Index (CVI), we applied the method proposed by Waltz and Bausell [29], where scores below 0.70 are considered unacceptable. Our obtained CVI of 0.72 surpasses this cutoff, demonstrating adequate content validity for the overall questionnaire.

Cronbach’s alpha coefficients were computed for knowledge (0.79) and various constructs of the HBM, including perceived susceptibility (0.78), perceived severity (0.83), perceived barriers (0.71), perceived benefits (0.85), and self-efficacy (0.82). The reliability of the questionnaire was assessed using a test-retest method. In this process, 30 eligible mothers completed the questionnaire twice, with a two-week interval between administrations, and the scores from both stages were compared. The reliability coefficient was found to be 0.8.

Data analysis

Quantitative variables were summarized using descriptive statistics, including standard deviation, mean, median, and range, while qualitative variables were reported as frequencies and percentages. Pearson’s correlation coefficient was utilized to create the correlation matrix for the model constructs, and multiple linear regression analysis was employed to examine the relationships among the model constructs, knowledge, and behavior. Prior to regression analysis, key assumptions were checked: (1) Linearity was assessed using scatterplots of residuals and partial regression plots; (2) No missing data were observed in this study, and sampling proceeded until the predetermined sample size was attained; and (3) Multicollinearity was evaluated using variance inflation factors (VIFs), with all values below 3 indicating no concerns. In constructing the regression model, the order of variable entry was determined through a combination of empirical and statistical considerations. First, we examined the bivariate correlations between independent variables and the outcome, prioritizing variables with stronger associations (p < 0.2) for initial inclusion. A stepwise regression approach was then applied to select the most significant predictors, ensuring an optimal balance between empirical data and the Health Belief Model framework. Demographic variables were examined as potential covariates in preliminary analyses. However, none showed statistically significant associations with mothers’ preventive behaviors in our sample (p > 0.05). Consequently, these variables were not included as covariates in the final regression model to maintain parsimony.

Path analysis was conducted to assess both the direct and indirect effects of the model constructs on the dependent variable. For data analysis, AMOS and SPSS21 software were used, with a significance level set at p < 0.05.