Relationship between the quality of dental services and the perceived quality of life among adolescents

Abstract

Objective  to analyze the relationship between the quality of dental services and the quality of life perceived by adolescents.

Methods  A cross-sectional quantitative epidemiological study, part of an epidemiological survey on oral health conditions among schoolchildren. Trained undergraduate students used a software developed for data collection using the QASO-A and KS-52 instruments and for building the database. The data were tabulated in MS Excel and later exported to the RStudio statistical software, version 4.3.2.

Results  The sample consisted of 409 adolescents (54.3% girls; 74.1% mixed-race; 68.7% from state schools). The QASO-A showed the highest score in the Evaluation scale (8.50 ± 4.36) and an overall mean of 18.88 ± 3.88. In the KIDSCREEN-52, Feelings had the highest mean (21.84 ± 5.01), while Peer Relations/Bullying had the lowest (4.94 ± 2.35); the overall mean score was 168.70. Reliability (Ω) was ≥0.70 in almost all scales, except for Physical Activities and Health (0.66), Self-Perception (0.57), and Peer Relations/Bullying (0.63). The SEM showed adequate fit; QASO-A factor loadings were strong, while those of the KS-52 ranged from -0.20 to 0.86. The association between satisfaction with dental care and quality of life was not significant (p = 0.09).

Conclusions  The results indicate that, despite the good model fit, the association between satisfaction with dental care and perceived quality of life was weak and not statistically significant, possibly due to the adolescents’ favorable epidemiological profile and the characteristics of the sample. Future studies should consider different age groups and socioeconomic contexts to enhance the generalizability of the findings.

Keywords
Health services research; Dentistry; Dental care; Quality of life

Introduction

Decades after the establishment of the Unified Health System (Sistema Único de Saúde – SUS), effective access to healthcare services remains a challenge, despite notable advances in primary care. The fulfillment of the constitutional right to universal, equitable, and comprehensive care is still constrained by complex structural, social, political, and economic factors, which have drawn growing multidisciplinary attention¹.

In this context, the utilization of health services is a cornerstone of the system, occurring through the interaction between users and providers and shaped by the interplay between individual care needs and professional actions². Analyzing health service utilization is fundamental in public health, as it reveals population profiles, prevalent health conditions, and patterns of demand, thereby informing the planning, formulation of policies, and organization of services³.

Several studies have examined access to oral health services, emphasizing the impact of the National Oral Health Policy (Política Nacional de Saúde Bucal – PNSB) on expanding service coverage⁴. Despite these advances, national data indicate that a significant portion of the Brazilian population continues to face barriers to accessing dental care, largely due to persistent social inequalities⁵. Furthermore, the organization of services and their perceived quality play a decisive role in influencing the decision to seek care⁶.

Studies in public health underscore the importance of understanding how socioeconomic, cultural, and organizational factors shape oral health. Such understanding is essential for developing effective public policies and strengthening comprehensive healthcare. Access to services should also be regarded as a driver of social transformation, fostering citizenship, belonging, and community participation in the improvement of oral health services⁷.

Evaluating the quality of healthcare services is a complex process that emerges from the interaction between users and providers, whose distinct perceptions must be considered in an integrated manner. Recognizing these perspectives is vital to aligning expectations, meeting the needs of both groups, and promoting the continuous improvement of healthcare delivery⁸. Donabedian⁹ identified patient satisfaction as one of the essential pillars for assessing service quality, alongside professional performance and active community participation. From this perspective, the concept of quality was structured into three interdependent yet distinct dimensions, which gradually evolved to incorporate users’ perspectives into the formulation of criteria and the definition of parameters used to measure care quality.

Research on dental service quality and user satisfaction is critical to strengthening the SUS, expanding access to public dental care, and reinforcing social accountability. User satisfaction serves as a key indicator of care quality, making systematic and continuous evaluations of health services indispensable. These assessments directly affect general health, quality of life, well-being, and oral health. Moreover, it is important to ensure that adolescents and young people are included in the construction of their therapeutic plans, thereby fostering greater engagement and collaboration with the healthcare team¹⁰,¹¹.

The complexity of the concept of quality of life has prevented the establishment of a single, universally accepted definition. In the 1990s, academic debate around its conceptualization intensified, aiming to reduce ambiguities and promote greater clarity¹². Within this context, the World Health Organization Quality of Life Group (WHOQOL) proposed a definition that acknowledges the inherent complexity and subjectivity of the concept. From this perspective, quality of life refers to an individual’s perception of their position in life, within the cultural and value frameworks in which they live, and in relation to their goals, expectations, standards, and concerns¹³.

Although these definitions are applicable to adolescents, it is crucial to consider the specificities of Health-Related Quality of Life (HRQoL) in this age group. Such consideration ensures that the instruments and methods employed are appropriately tailored to their cognitive and developmental characteristics. This adaptation is fundamental to preserving the accuracy and validity of the evaluations conducted¹⁴. In light of this context, the present study seeks to analyze the relationship between the quality of dental services and perceived HRQoL among adolescents, considering the interactions among the variables involved through structural equation modeling.

Methods

This is an excerpt from the “Epidemiological Survey on Oral Health Conditions among Schoolchildren in Montes Claros, Minas Gerais, Brazil” (SB Moc Project), a cross-sectional quantitative epidemiological study. The study population consisted of students from public schools located in the urban area of Montes Claros, Minas Gerais, a medium-sized municipality with an estimated population of 434.321 inhabitants¹⁵. The survey followed the methodological proposal of the World Health Organization (WHO), published in its fifth edition in 2013, which established standardized protocols for conducting epidemiological investigations in oral health¹⁶. The 2013 edition recommends the use of two age indices, 12 and 15 years, for the evaluation of oral health conditions in adolescents¹⁶. The age group corresponding to the 12-year age index includes individuals between 11 years and 6 months and 12 years and 6 months, while the 15-year age index encompasses those between 14 years and 6 months and 15 years and 6 months.

Initially, a probability sampling by clusters was outlined, with the target population being 12- and 15-year-old students enrolled in 2019 in public (municipal and state) and private educational institutions located in both urban and rural areas of the municipality. The sample was stratified by age group, according to the WHO methodological recommendation¹⁶, ensuring the representativeness of adolescents from the municipality. The following parameters were adopted in the sample planning for categorical variables, such as those related to the quality of dental care: the target population (5539 eligible 12-year-old students and 5228 15-year-old students), a prevalence of 50% for health-related events or conditions, a 95% confidence level (Z = 1.96), a 5% sampling error, a 10% non-response rate, and a design effect (deff = 1.4)¹⁷,¹⁸. It would have been necessary to evaluate 540 ((360*1,4) + 10%) and 537 ((358*1.4) + 10%) students, respectively. However, the COVID-19 pandemic, which began in 2019, led to the suspension of data collection, necessitating a reassessment of the sample size calculation, considering only municipal and state public schools in the urban area. The study received approval from the State and Municipal Education Secretariats and the Municipal Health Secretariat. Schools within the municipality were randomly selected, targeting adolescents within the age ranges recommended by the WHO. The school administrations were informed about the project and authorized participation. Subsequently, informed consent was obtained from the guardians, and assent was obtained from the students¹⁸. Adolescents with cognitive limitations that would prevent them from participating in the interviews, as well as those who did not sign the assent form, were excluded from the study.

During the study, the Research Management System (Sistema de Gerenciamento de Pesquisas - SGP) was used, developed by a specialized company hired to create two specific software programs: one for data collection and database construction, with interfaces designed for both oral examinations and interviews, and another for estimating intra- and inter-examiner calibration. In this segment of the SB Moc Project, the field research phase utilized the SGP module focused on data collection and structuring through the interview interface. The instruments applied were the Quality of Care in Dental Services from the Perspective of Adolescents (QASO-A), considered valid according to the study by Lopes Júnior et al.¹⁹ (2026), and the KIDSCREEN-52 (KS-52), widely used to assess quality of life among adolescents²⁰,²¹. The interviews were conducted by undergraduate dental students from the partner institutions, and all participants who required dental care were referred to their respective Family Health Units to ensure access to treatment. This study was approved by the National Research Ethics Committee of the State University of Montes Claros (Unimontes - Opinion No. 2.483.638).

The data were tabulated in MS-Excel and then exported to the statistical software RStudio version 4.3.2²². The first procedure was the statistical analysis of the sociodemographic data. For categorical data (sex, age, type of school, race/ethnicity), both absolute frequency (n) and relative frequency in percentages were used. For continuous data (scores of the QASO-A and KS-52 dimensions), means and standard deviations were calculated²³. For the KS-52, the syntax from the Instrument’s Manual²⁴ was used to obtain the dimension scores and the total score. After this procedure, descriptive statistics were performed, followed by factor analysis.

To assess the plausibility of the model, Structural Equation Modeling (SEM) was used, which is an extension of linear regression techniques that allows for multiple dependent variables, whether observable or latent²⁵,²⁶. The model in this study aims to verify whether there is an impact of satisfaction with dental care, measured by the QASO-A, on HRQoL, measured by the KS-52.

The first procedure in these analyses was to perform the Bartlett’s sphericity test (which should be significant for p < 0.05), which checks if the data matrix is factorable, and the Kaiser-Meyer-Olkin (KMO) test to assess the sample adequacy (desirable values above 60%) for factor analyses. The result of the sphericity test was p = 0.00 for both instruments, and they were considered suitable for analysis, with the KMO value for the QASO-A being 0.91 and for the KS-52, 0.85.

After this procedure, the analysis was performed using the estimation method of Weighted Least Squares Mean and Variance adjusted (WLSMV), which is the appropriate estimator for ordinal and nominal categorical data²⁷. Considering that many missing data were detected in both instruments, the technique used to handle these missing data was pairwise, which uses only the available information for each pair of variables being analyzed, without completely excluding cases with missing data, allowing the model to utilize as much data as possible and reducing the loss of information²⁸,²⁹. To assess the plausibility of the model, it is necessary to verify whether the fit indices are adequate. The fit indices used were: χ²; χ²/gl; Comparative Fit Index (CFI); Tucker-Lewis Index (TLI); Standardized Root Mean Residual (SRMR); and Root Mean Square Error of Approximation (RMSEA). χ² values should not be significant (p > 0.05); the χ²/gl ratio should be less than or equal to 5.00 and ideally less than 3.00; CFI and TLI values should be minimally above 0.90 and preferably above 0.95; RMSEA values should be below 0.08 or preferably below 0.06, with the confidence interval (upper limit) < 0.10²⁶,³⁰,³¹. The significance level of the present study was set at p < 0.05.

Results

The analyzed sample comprised 409 subjects, predominantly female (54.30%), predominantly self-identified as mixed-race (74.10%), and enrolled in state schools (68.70%), as presented in Table 1.

Table 1
Sociodemographic characteristics of the 409 participants the excerpt from the SB Moc Project in Montes Claros, Minas Gerais, Brazil.

Demographic Variable	Frequency	Percentage
Type of School
State School	281	68.70
Municipal School	126	30.80
Missing	2	0.48
Sex
Male	187	45.70
Female	222	54.30
Missing	0	0.00
Self-reported Ethnicity and Race
White	36	8.80
Black	57	13.90
Indigenous	4	1.00
Mixed-race	303	74.10
Missing	9	2.20
Age
12 Years	122	29.80
15 Years	287	70.20
Missing	0.00	0.00

Regarding the calculated scores of the instruments (Table 2), the QASO-A had the highest score in the Assessment scale (8,50 ± 4,36) and a total score of 18.88 ± 3,88. For the KS-52, the dimension with the highest score was the Psychological Well-Being dimension (21,84 ± 5,01), while the lowest score was in the Bullying dimension (4,94 ± 2,35), and the average total score was 168.70 ± 168,70.

Table 2
Descriptive statistics of the total scores and dimensions of the quality of life instruments for the 409 adolescents surveyed the excerpt from the SB Moc Project in Montes Claros, Minas Gerais, Brazil.

Instrument Dimension	Missing data	Mean	Standard Deviation	95% Confidence Interval of the Mean
				Lower Limit	Upper Limit
QASO-A
Satisfaction	58	8.50	1.81	8.69	8.31
Assessment	170	10.63	4.36	11.18	10.07
Total score	196	18.88	3.88	19.41	18.36
Kidscreen-52
Physical Well-Being	0	14.53	3.62	14.88	14.18
Psychological Well-Being	0	21.84	5.01	22.33	21.35
Parent Relations and Home Life	0	22.85	5.09	23.35	22.36
Peers and Social Support	0	22.92	4.70	23.38	22.46
School Environment	0	21.74	4.42	22.17	21.31
Moods and Emotions	0	13.55	5.51	14.08	13.01
Self-perception	0	16.88	2.64	17.14	16.63
Autonomy	0	18.91	4.37	19.34	18.49
Financial Resources	0	10.55	3.16	10.86	10.24
Bullying	0	4.93	2.35	5.16	4.70
Total score Kidscreen-52	0	168.70	18.70	170.52	166.89

Regarding the reliability of the instruments, assessed by the internal consistency reliability measure and evaluated through McDonald’s Ω coefficient (Table 3), the QASO-A in its two scales obtained values greater than 0.70, while for the KS-52, only the Physical Well-Being (0.66), Self-perception (0.57), and Bullying (0.63) scales did not reach 0.70 in this test.

Table 3
Internal Consistency Reliability of the QASO-A and KS-52 by McDonald’s Ω Coefficient.

Instrument Dimension	Number of Items in the Scale	Ω McDonald
QASO-A
Satisfaction	15	0.85
Assessment	9	0.73
Kidscreen-52
Physical Well-Being	5	0.66
Psychological Well-Being	6	0.85
Parent Relations and Home Life	6	0.82
Peers and Social Support	6	0.75
School Environment	6	0.75
Moods and Emotions	7	0.85
Self-perception	5	0.57
Autonomy	5	0.80
Financial Resources	3	0.80
Bullying	3	0.63

In the SEM analysis, it was observed that the model achieved adequate fit values, indicating that it is plausible, as shown in Table 4. All factor loadings (except the factor loading between the latent scores of the QASO-A and the KS-52) were significant. Figure 1 illustrates the model, and its analysis indicates that although the model is considered feasible, the association between satisfaction with dental care and self-perception of HRQoL is not significant (p = 0.09) and is very weak. The factor loadings of the QASO-A with its dimensions can be considered strong, and for the KS-52, they ranged from -0.20 (Bullying) to 0.86 (Family/Family Activities), indicating that they are generally high (factor loading > 0.60).

Table 4
Demonstration of Fit Indices for Structural Equation Modeling Tested Using the Weighted Least Squares Mean and Variance Adjusted Method.

Indicators	p − χ2	χ2 (gl)	χ2/gl	CFI	TLI	SRMR	RMSEA (90% IC)
Values	<0.01	604 (166)	3.63	0.97	0.96	0.07	0.06 (0.06 – 0.06)

• Legend: χ² = chi-square; gl = degrees of Freedom; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual; RMSEA = Root Mean Square Error of Approximation.

Figure 1
Path diagram highlighting the relationship between the QASO-A and the Kidscreen-52, through structural equation modeling with the Weighted Least Squares Mean and Variance Adjusted estimator. Legend: *Not significant for p < 0,05. PHY (Physical Well-Being); PWB (Psychological Well-Being); EMO (Moods and Emotions); FIN (Financial Resources); PAR (Parent Relations and Home Life); AUT (Autonomy); SEL (Self-perception); SCH (School Environment); SOC (Peers and Social Support); BUL (Bullying).

Discussion

The QASO-A instrument demonstrated adequate reliability indices, with Ω values greater than 0.7 in both of its scales. In contrast, for the KS-52 instrument, only three scales did not reach the generally accepted minimum cutoff of 0.7: Physical Well-Being (Ω = 0.66), Self-perception (Ω = 0.57), and Bullying (Ω = 0.63), which may indicate lower homogeneity of items within these dimensions. However, it should be noted that some studies³²,³³ consider reliability values lower than 0.7, but close to 0.6, as satisfactory.

The results obtained through SEM indicated that the proposed model showed satisfactory fit indices, suggesting both theoretical and statistical plausibility of the evaluated hypothetical structure. Most of the factor loadings were statistically significant, with the exception of the relationship between the latent scores of the QASO-A and the KS-52, which may reflect a limitation in the strength of the direct association between satisfaction with dental care and perceived HRQoL. This observation is corroborated by previous studies¹³,³⁴, which point to the multifactorial nature of quality of life in adolescents, being strongly influenced by contextual, psychosocial, and familial aspects, and not solely by experiences with health services.

The analysis also revealed that the association between satisfaction with dental care and self-perception of HRQoL was not statistically significant (p = 0.09), and showed a weak magnitude. Due to the gap in the scientific literature on this topic, no previous studies were identified that described such an association, which hindered the comparison of the obtained results. However, it is possible that this finding reflects the specificities of dental care experiences among adolescents. Considering that, in general, there has been a significant improvement in the epidemiological profile of oral health in this age group, it is expected that this population will require fewer dental visits and that procedures will be less invasive.

The final report of the national oral health survey, SB Brasil³⁵, conducted in 2023, shows that 49.88% of 12-year-olds were free of cavities (Decayed, Missing and Filled Teeth Index - DMFT=0), with the highest percentages in the South (59.62%) and Southeast (57.62%) regions, where the municipality of this study is located. Furthermore, 33.80% of adolescents aged 15 to 19 years were cavity-free, with 37.64% in the Southeast region specifically. Data on tooth pain experience also reinforce this assumption, as 16.97% of 12-year-olds and approximately 20% of adolescents aged 15 to 19 years reported experiencing tooth pain in the last 6 months. Additionally, the missing component of the DMFT at 12 years old in Brazil represented 4.41%, and 16.04% for 15 to 19-year-olds. These data reflect the low prevalence of dental outcomes through tooth extraction, which is one of the most invasive procedures in dentistry and may influence satisfaction perception with dental care.

This result may also reflect the characteristics of the sample, since only adolescents enrolled in public schools were included in the study. In research conducted by Zambaldi et al.³⁶ (2021), it was observed that the type of school was associated with having attended a first dental appointment, with students from private institutions being nearly four times more likely to have received dental care compared to those from public schools. Similar findings were reported by Oliveira et al.³⁷ (2015), who found that students from private schools accessed health services 29% more frequently than those from public schools, highlighting the influence of better socioeconomic conditions on this behavior. Therefore, the results appear to reflect sample limitations, more plausibly than the absence of an effect of dental service quality on adolescents’ HRQoL.

The factor loadings observed between the QASO-A and its respective dimensions, Satisfaction and Assessment, showed high magnitudes, indicating a strong relationship with the quality of dental services. Previous studies³⁸,³⁹highlight that identifying and systematically analyzing the multiple factors influencing user satisfaction are fundamental elements for the restructuring and continuous improvement of health services. This ongoing evaluative process is essential for implementing adjustments that not only meet patients’ expectations but are also aligned with their actual needs, contributing to more humanized, effective, and responsive health care.

In general, the magnitude of the factor loadings for the KS-52 instrument dimensions can be considered high, as most values exceed the commonly accepted cutoff point in the literature for satisfactory interpretations (factor loading > 0.6). These results help to understand the contribution of these dimensions to determining adolescents’ HRQoL levels. The literature supports these contributions, such as the Physical Well-Being dimension, as regular physical activity is associated with several health benefits, including improvements in glycemic and lipid profiles, reduction in systemic blood pressure, and weight loss, effects that are enhanced when combined with a balanced diet⁴⁰,⁴¹. However, previous studies have observed a trend among adolescents toward decreasing both the duration and intensity of physical exercise⁴²,⁴³.

In previous investigations, the items related to Psychological Well-Being and Moods and Emotions address both the presence of positive emotions, such as pleasure, joy, and life satisfaction, and the perception of specific emotions associated with depressive and stress-related states²⁰. The biopsychosocial transformations characteristic of this developmental stage contribute to an increased negative perception regarding psychological aspects⁴⁴.

Another dimension of the KS-52 considers the Parent Relations and Home Life. In this regard, it is essential to recognize the determining role of this setting in the physical, psychological, and emotional development of individuals, especially during the early stages of life⁴⁵. The family constitutes the primary context of socialization, serving as the foundation for the formation of emotional bonds and the social competencies that will be progressively developed throughout life⁴⁶.

Self-perception is also considered by the instrument. Satisfaction with body image, associated with high levels of self-esteem and self-confidence, can positively influence various dimensions of quality of life, directly impacting mood, emotional regulation, and the quality of social interactions⁴⁷. In this way, friendships also correspond to a dimension of the KS-52. The study by Reininger et al.⁴⁸ (2012) identified an association between social support, social integration, and quality of life with mental health conditions. It was highlighted that disruptions in these aspects, as well as negative social self-perception, can contribute to the onset of anxiety and depression, which, if not adequately managed, can evolve into more severe psychopathological conditions during adolescence.

Experiences in the school environment are covered by another dimension of the instrument. Studies indicate that these experiences, as well as levels of satisfaction with school, have a significant influence on overall life satisfaction and individuals’ subjective well-being⁴⁹.

However, despite the instrument showing high factor loadings in most dimensions, two of them, Financial Resources and Bullying, demonstrated low factor loadings, indicating a smaller weight of these latent variables on HRQoL. Nevertheless, regarding the financial resources dimension, the literature points to the existence of this association. Studies⁵⁰,⁵¹ consistently show that financial pressure has been associated with more depressive symptoms. In this study, the result differs, possibly due to the sample’s characteristics, as data were collected only from public schools, presumably with most students from lower-income families. The study by Zacchi et al.⁵² (2016), which investigated the influence of family socioeconomic resources on student performance in the 2011 ENEM exam, pointed out that students from families with a monthly income of up to five minimum wages are mostly concentrated in public state schools, while those from families with income higher than five minimum wages are predominantly enrolled in private institutions and federal schools, such as Federal Institutes, University-linked Application Colleges, and Federal Centers for Technological Education (CEFETs). A sample with adolescents from more diverse family income levels would likely show an association between economic issues and perceived quality of life.

Regarding the Bullying dimension, the age restriction of the adolescents in the sample likely contributed to this result, as there is evidence of this association in the literature. A study⁵³ conducted between 2011 and 2012 in Mexico demonstrated a prevalence of 8.4% of bullying experiences among children and adolescents. These experiences were associated with lower HRQoL scores and may be linked to the onset of mental health conditions during adolescence.

Considering the significant improvement in the oral health epidemiological profile in this age group in recent years, it is plausible to assume that this population requires fewer dental visits, with a predominance of less invasive procedures. Furthermore, the findings should be interpreted in light of the characteristics of the investigated sample, which consisted exclusively of adolescents enrolled in public schools, which may have influenced the findings and limit the generalizability of the results.

The SEM results indicated that the proposed model demonstrated satisfactory fit and robust factor loadings, supporting its consistency in assessing dental care quality and perceived HRQoL. However, the association between satisfaction with dental care and self-perceived HRQoL was weak and not statistically significant, which may reflect the specific experiences of adolescents and the limitations of the sample rather than the absence of an effect. Future research should extend these analyses to broader and more diverse populations, with greater exposure to dental services, to better evaluate the generalizability of the findings.

The authors acknowledge the financial support provided by the Minas Gerais Research Foundation (FAPEMIG) through the Researcher from Minas Gerais Program (PPM), grant number PPM-00513-18.

References

• ¹ Viegas APB, Carmo RF, Luz ZMP. [Factors associated to the access to health services from the point of view of professionals and users of basic reference unit].Saúde Soc.2015 jan-mar;24(1):100-12. Portuguese. doi: 10.1590/S0104-12902015000100008.
• ² Travassos C, Martins M. [A review of concepts in health services access and utilization]. Cad Saude Publica. 2004;20 Suppl 2:S190-8. Portuguese. doi: 10.1590/s0102-311x2004000800014.
  » https://doi.org/10.1590/s0102-311x2004000800014
• ³ Pinto RS, Matos DL, Loyola Filho AI. [Characteristics associated with the use of dental services by the adult Brazilian population]. Cien Saude Colet. 2012 Feb;17(2):531-44. Portuguese. doi: 10.1590/s1413-81232012000200026.
  » https://doi.org/10.1590/s1413-81232012000200026
• ⁴ Fonseca EP, Fonseca SGO, Meneghim MC. Analysis of public dental services access in Brazil. ABCS Health Sci. 2017 Aug;42(2). Available from: https://www.portalnepas.org.br/abcshs/article/view/1008
  » https://www.portalnepas.org.br/abcshs/article/view/1008
• ⁵ Rocha-Madruga RC, Ferreira RC, Vargas AMD, Ferreira EF, Xavier AFD, Martins AMEBL, et al. Access to oral health services in areas covered by the family health strategy, Paraíba, Brazil. Pesqu Bras Odontopediatr Clin Integr. 2017;17(1):e3006. doi: 10.4034/PBOCI.2017.171.06.
  » https://doi.org/10.4034/PBOCI.2017.171.06
• ⁶ Carreiro DL, Souza JGS, Coutinho WLM, Ferreira RC, Ferreira EFE, Martins AMEBL. [The use of dental services on a regular basis in the population of Montes Claros in the State of Minas Gerais, Brazil]. Cien Saude Colet. 2017 Dec;22(12):4135-50. Portuguese. doi: 10.1590/1413-812320172212.04492016.
  » https://doi.org/10.1590/1413-812320172212.04492016
• ⁷ Antunes JLF, Narvai PC. [Dental health policies in Brazil and their impact on health inequalities]. Rev Saude Publica. 2010;44(2):360-5. Portuguese. doi: 10.1590/S0034-89102010005000002.
  » https://doi.org/10.1590/S0034-89102010005000002
• ⁸ Righi AW; Schmidt AS; Venturini JC. [Quality in public health services - an assessment of the family health strategy]. Rev Prod Online. 2010;10(3):649-69.
• ⁹ Donabedian A. The seven pillars of quality. Arch Pathol Lab Med. 1990 Nov;114(11):1115-8.
• ¹⁰ Nóbrega DF, Souza JGS, Assis ACBM, Martins AMEBL, Bulgareli JV. [The association between normative and subjective oral health conditions and dissatisfaction with dental services among adult Brazilians]. Cien Saude Colet. 2018 Nov;23(11):3881-90. Portuguese. doi: 10.1590/1413-812320182311.28892016.
  » https://doi.org/10.1590/1413-812320182311.28892016
• ¹¹ Viana IB, Moreira RDS, Martelli PJL, Oliveira ALS, Monteiro IDS. Evaluation of the quality of oral health care in Primary Health Care in Pernambuco, Brazil, 2014. Epidemiol Serv Saude. 2019 Jul 4;28(2):e2018060. doi: 10.5123/S1679-49742019000200015.
• ¹² Soares AH, Martins AJ, Lopes MC, Britto JA, Oliveira CQ, Moreira MC. [Quality of life of children and adolescents: a bibliographical review]. Cien Saude Colet. 2011 Jul;16(7):3197-206. Portuguese.
• ¹³ Fleck MPA. [The World Health Organization instrument to evaluate quality of life (WHOQOL-100): characteristics and perspectives]. Cien Saude Colet. 2000;5(1):33-8. Portuguese. doi: 10.1590/S1413-81232000000100004.
  » https://doi.org/10.1590/S1413-81232000000100004
• ¹⁴ Matza LS, Swensen AR, Flood EM, Secnik K, Leidy NK. Assessment of health-related quality of life in children: a review of conceptual, methodological, and regulatory issues. Value Health. 2004 Jan-Feb;7(1):79-92. doi: 10.1111/j.1524-4733.2004.71273.x.
  » https://doi.org/10.1111/j.1524-4733.2004.71273.x
• ¹⁵ Brazilian Institute of Geography and Statistics. [Panorama Montes Claros]. Brasília: IBGE; 2024 [cited 2025 jan 4]. Available from: https://cidades.ibge.gov.br/brasil/mg/montes-claros/panorama Portuguese.
  » https://cidades.ibge.gov.br/brasil/mg/montes-claros/panorama
• ¹⁶ World Health Organization. Oral health surveys: basic methods. Geneva: WHO; 2013.
• ¹⁷ Triola MF. Introdução à estatística. 7th ed. Rio de Janeiro: LTC; 1999.
• ¹⁸ Martins AMEBL, Ferreira AC, Nicolau LCS, Lima PXV, Maia MN, Silva LF, et al. [Sampling of oral conditions survey during COVID-19 pandemic: methodologic study]. Res Soc Dev. 2022;11(1):1-15. Portuguese. doi: 10.33448/rsd-v11i1.24896.
  » https://doi.org/10.33448/rsd-v11i1.24896
• ¹⁹ Lopes Júnior CWX, Soares JRD, Santos ASF, Reis C dos, Sales MSM, Oliveira MP, et al.. Validation of an instrument that assesses the quality of care in dental services from the perspective of adolescents. Braz J Oral Sci [Internet]. 2026;25:e268908. Available from: https://doi.org/10.20396/bjos.v25i00.8678908
  » https://doi.org/10.20396/bjos.v25i00.8678908
• ²⁰ Gaspar T, Matos MG. [Quality of life in children and adolescents: Portuguese version of the KIDSCREEN 52 instruments.]. Lisboa: Aventura Social e Saúde; 2008. Portuguese.
• ²¹ Gaspar T, Matos MG, Leal I. [Quality of life and well-being among children and adolescents] Rev Bras Ter Cogn. 2006;2(2):47-60. Portuguese.
• ²² RStudio Team. RStudio 4.3.2: Integrated Development for R. RStudio; 2024. Posit Support. Available from: http://www.rstudio.com
  » http://www.rstudio.com
• ²³ Vieira SS. Bioestatística. 4th ed. Rio de Janeiro: Guanabara Koogan; 2018.
• ²⁴ Ravens-Sieberer U, Kidscreen Group Europe, editors. The Kidscreen questionnaires: quality of life questionnaires for children and adolescents: handbook. 3rd ed. Lengerich: Pabst Science Publishers; 2016.
• ²⁵ Brown TA. Confirmatory factor analysis for applied research. 2nd ed. New York, NY: The Guilford Press; 2015. p. xvii, 462.
• ²⁶ Hair JF Jr, Black WC, Babin BJ, Anderson RE, Tatham RL. Análise multivariada de dados. 6th ed. Porto Alegre: Bookman; 2009.
• ²⁷ DiStefano C, Morgan GB. A comparison of diagonal weighted least squares robust estimation techniques for ordinal data. Struct Equ Modeling. 2014 Jul;21(3):425-38. doi: /10.1080/10705511.2014.915373.
  » https://doi.org//10.1080/10705511.2014.915373
• ²⁸ Enders CK. Applied missing data analysis. New York: Guilford Publications; 2017.
• ²⁹ Enders CK. Missing data: an update on the state of the art. Psychol Methods. 2025 Apr;30(2):322-39. doi: 10.1037/met0000563.
  » https://doi.org/10.1037/met0000563
• ³⁰ Cheung GW, Rensvold RB. Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance. Struct Equ Modeling. 2002 Apr;9(2):233-55. doi: 10.1207/S15328007SEM0902_5.
• ³¹ Maaz TM, Heck RH, Glazer CT, Loo MK, Zayas JR, Krenz A, et al. Measuring the immeasurable: a structural equation modeling approach to assessing soil health. Sci Total Environ. 2023 Apr 20;870:161900. doi: 10.1016/j.scitotenv.2023.161900.
  » https://doi.org/10.1016/j.scitotenv.2023.161900
• ³² Streiner DL. Starting at the beginning: an introduction to coefficient alpha and internal consistency. J Pers Assess. 2003 Feb;80(1):99-103. doi: 10.1207/S15327752JPA8001_18.
• ³³ Balbinotti MAA, Barbosa MLL. [Reliability and confirmatory factorial analysis of the IMPRAFE-126 with gauchos' practitioners of physical activities]. Psico-USF. 2008 jan-jun;13(1):1-12. Portuguese.
• ³⁴ Bramston P, Chipuer H, Pretty G. Conceptual principles of quality of life: an empirical exploration. J Intellect Disabil Res. 2005 Oct;49(Pt 10):728-33. doi: 10.1111/j.1365-2788.2005.00741.x.
• ³⁵ Brazilian Ministry of Health. Secretariat of Primary Health Care. Department of Strategies and Policies for Community Health. [SB Brasil 2023: National Oral Health Survey: Final Report]. Brasília: Ministry of Health; 2024. 537 p. Portuguese.
• ³⁶ Zambaldi MPM, Molina MCB, Prado CB, Santos Neto ET. [Access to oral health goods and services for school children from 7 to 10 years in Vitória-ES]. Rev Odontol UNESP. 2021;50:e20210030. Portuguese. doi: 10.1590/1807-2577.03021.
  » https://doi.org/10.1590/1807-2577.03021
• ³⁷ Oliveira MM, Andrade SS, Campos MO, Malta DC. [Factors associated with the demand for health services by Brazilian adolescents: the National School Health Survey (PeNSE), 2012]. Cad Saude Publica. 2015 Aug;31(8):1603-14. Portuguese. doi: 10.1590/0102-311X00165214.
• ³⁸ Martins AM, Jardim LA, Souza JG, Rodrigues CA, Ferreira RC, Pordeus IA. Is the negative evaluation of dental services among the Brazilian elderly population associated with the type of service? Rev Bras Epidemiol. 2014 Jan-Mar;17(1):71-90. doi: 10.1590/1415-790x201400010007eng.
• ³⁹ Martins AM, Ferreira RC, Santos-Neto PE, Carreiro DL, Souza JG, Ferreira EF. Users' dissatisfaction with dental care: a population-based household study. Rev Saude Publica. 2015;49:51. doi: 10.1590/S0034-8910.2015049005659.
  » https://doi.org/10.1590/S0034-8910.2015049005659
• ⁴⁰ Stevenson ET, Davy KP, Seals DR. Hemostatic, metabolic, and androgenic risk factors for coronary heart disease in physically active and less active postmenopausal women. Arterioscler Thromb Vasc Biol. 1995 May;15(5):669-77. doi: 10.1161/01.atv.15.5.669.
  » https://doi.org/10.1161/01.atv.15.5.669
• ⁴¹ Rauramaa R, Salonen JT, Kukkonen-Harjula K, Seppänen K, Seppälä E, Vapaatalo H, et al. Effects of mild physical exercise on serum lipoproteins and metabolites of arachidonic acid: a controlled randomised trial in middle aged men. Br Med J (Clin Res Ed). 1984 Feb;288(6417):603-6. doi: 10.1136/bmj.288.6417.603.
  » https://doi.org/10.1136/bmj.288.6417.603
• ⁴² Armstrong N. The challenge of promoting physical activity. J R Soc Health. 1995 Jun;115(3):187-92. doi: 10.1177/146642409511500314.
  » https://doi.org/10.1177/146642409511500314
• ⁴³ Lazzoli JK, et al. [Physical activity and health in childhood and adolescence]. Rev Bras Med Esporte. 1998;4(4):107-9. Portuguese.
• ⁴⁴ Agathão BT, Reichenheim ME, Moraes CL. Health-related quality of life of adolescent students. Cien Saude Colet. 2018 Feb;23(2):659-68. doi: 10.1590/1413-81232018232.27572016.
  » https://doi.org/10.1590/1413-81232018232.27572016
• ⁴⁵ Osório LC. [Family today]. Porto Alegre: Artes Médicas; 1996. Portuguese.
• ⁴⁶ Dessen MA, Polonia AC. [The family and the school as contexts for human development]. Paidéia. 2007;17(36):21-32. Portuguese.
• ⁴⁷ Arcila-Arango JC, Castro-Sánchez M, Espoz-Lazo S, Cofre-Bolados C, Zagalaz-Sánchez ML, Valdivia-Moral P. Analysis of the dimensions of quality of life in Colombian University students: structural equation analysis. Int J Environ Res Public Health. 2020 May;17(10):3578. doi: 10.3390/ijerph17103578.
• ⁴⁸ Reininger BM, Pérez A, Aguirre Flores MI, Chen Z, Rahbar MH. Perceptions of social support, empowerment and youth risk behaviors. J Prim Prev. 2012 Feb;33(1):33-46. doi: 10.1007/s10935-012-0260-5.
• ⁴⁹ Suldo S, Thalji-Raitano A, Gelley H, Hoy B. Understanding middle school students' life satisfaction: Does school climate matter? Appl Res Qual Life. 2013;8:169-82.
• ⁵⁰ Montoro JP, Ceballo R. Latinx adolescents facing multiple stressors and the protective role of familismo. Cultur Divers Ethnic Minor Psychol. 2021 Oct;27(4):705-16. doi: 10.1037/cdp0000461.
  » https://doi.org/10.1037/cdp0000461
• ⁵¹ Stein GL, Jensen M, Christophe NK, Cruz RA, Martin Romero M, Robins R. Shift and persist in mexican american youth: a longitudinal test of depressive symptoms. J Res Adolesc. 2022 Dec;32(4):1433-51. doi: 10.1111/jora.12714.
  » https://doi.org/10.1111/jora.12714
• ⁵² Zacchi RC, Ney MG, Ponciano NJ. [Educational inequalities in basic education: an investigation based on the National High School Examination]. Rev Vértices. 2016;18(1):79-108. doi: 10.19180/1809-2667.v18n116-05. Portuguese.
  » https://doi.org/10.19180/1809-2667.v18n116-05
• ⁵³ Hidalgo-Rasmussen CA, Ramírez-López G, Rajmil L, Skalicky A, Martín AH. Bullying and health-related quality of life in children and adolescent Mexican students. Cien Saude Colet. 2018 Jul;23(7):2433-41. doi: 10.1590/1413-81232018237.16392016.
  » https://doi.org/10.1590/1413-81232018237.16392016

• Data Availability:
  Datasets related to this article will be available upon request to the corresponding author.

Edited by

• Editor:
  Dr. Altair A. Del Bel Cury

Data availability

Datasets related to this article will be available upon request to the corresponding author.

Publication Dates

• Publication in this collection
  14 Sept 2026
• Date of issue
  2026

History

• Received
  02 July 2025
• Accepted
  20 Nov 2025