Cross-cultural adaptation and psychometric properties of the Brazilian-Portuguese version of the Organizational Readiness for Implementing Change questionnaire

Machine learning to predict untreated dental caries in adolescents

OBJECTIVE

This study aimed to predict adolescents with untreated dental caries through a machine-learning approach using three different algorithms METHODS: Data came from an epidemiological survey in the five largest cities in Mato Grosso do Sul, Brazil. Data on sociodemographic characteristics, consumption of unhealthy foods and behaviours (use of dental floss and toothbrushing) were collected using Sisson's theoretical model, in 615 adolescents. For the machine learning, three different algorithms were used: (1) XGboost; (2) decision tree and (3) logistic regression. The epidemiological baseline was used to train and test predictions to detect individuals with untreated dental caries, through eight main predictor variables. Analyzes were performed using the R software (R Foundation for Statistical Computing, Vienna, Austria). The Ethics Committee approved the study..

RESULTS

For the 615 adolescents, xgboost performed better with an area under the curve (AUC) of 84% versus 81% for the decision tree algorithm. The most important variables were the use of dental floss, unhealthy food consumption, self-declared race and exposure to fluoridated water.

CONCLUSIONS

Family health teams can improve the work process and use artificial intelligence mechanisms to predict adolescents with untreated dental caries, and, in this way, schedule dental appointments for the treatment of adolescents earlier.

Psychometric properties of two implementation measures: Normalization MeAsure Development questionnaire (NoMAD) and organizational readiness for implementing change (ORIC)

Background

Effective interventions need to be implemented successfully to achieve impact. Two theory-based measures exist for measuring the effectiveness of implementation strategies and monitor implementation progress. The Normalization MeAsure Development questionnaire (NoMAD) explores the four core concepts (Coherence, Cognitive Participation, Collective Action, Reflexive Monitoring) of the Normalization Process Theory. The Organizational Readiness for Implementing Change (ORIC) is based on the theory of Organizational Readiness for Change, measuring organization members' psychological and behavioral preparedness for implementing a change. We examined the measurement properties of the NoMAD and ORIC in a multi-national implementation effectiveness study.

Method

Twelve mental health organizations in nine countries implemented Internet-based cognitive behavioral therapy (iCBT) for common mental disorders. Staff involved in iCBT service delivery (n = 318) participated in the study. Both measures were translated into eight languages using a standardized forward-backward translation procedure. Correlations between measures and subscales were estimated to examine convergent validity. The theoretical factor structures of the scales were tested using confirmatory factor analysis (CFA). Test-retest reliability was based on the correlation between scores at two time points 3 months apart. Internal consistency was assessed using Cronbach's alpha. Floor and ceiling effects were quantified using the proportion of zero and maximum scores.

Results

NoMAD and ORIC measure related but distinct latent constructs. The CFA showed that the use of a total score for each measure is appropriate. The theoretical subscales of the NoMAD had adequate internal consistency. The total scale had high internal consistency. The total ORIC scale and subscales demonstrated high internal consistency. Test-retest reliability was suboptimal for both measures and floor and ceiling effects were absent.

Conclusions

This study confirmed the psychometric properties of the NoMAD and ORIC in multi-national mental health care settings. While measuring on different but related aspects of implementation processes, the NoMAD and ORIC prove to be valid and reliable across different language settings.

Last dental visit and severity of tooth loss: a machine learning approach

The aims of the present study were to investigate last dental visit as a mediator in the relationship between socioeconomic status and lack of functional dentition/severe tooth loss and use a machine learning approach to predict those adults and elderly at higher risk of tooth loss. We analyzed data from a representative sample of 88,531 Brazilian individuals aged 18 and over. Tooth loss was the outcome by; (1) functional dentition and (2) severe tooth loss. Structural Equation models were used to find the time of last dental visit associated with the outcomes. Moreover, machine learning was used to train and test predictions to target individuals at higher risk for tooth loss. For 65,803 adults, more than two years of last dental visit was associated with lack of functional dentition. Age was the main contributor in the machine learning approach, with an AUC of 90%, accuracy of 90%, specificity of 97% and sensitivity of 38%. For elders, the last dental visit was associated with higher severe loss. Conclusions. More than two years of last dental visit appears to be associated with a severe loss and lack of functional dentition. The machine learning approach had a good performance to predict those individuals.

Health ombudsman and racial inequities in Dental Specialities Centers performance in Brazil: A multilevel analysis

OBJECTIVE

This study aimed to verify racial differences in the performance of Dental Specialities Centers in Brazil, according to the presence of active health ombudsman on four primary outcomes: (1) access and dental appointment, (2) reception services, (3) bond and responsibility, and (4) social participation.

METHODS

Data came from the PMAQ-CEO national evaluation of public healthcare services, 2018-2019. The two main explanatory variables were the self-classified race at the individual level and the presence of the health ombudsman at the second level (level of services provision). Individual covariates included age, sex and schooling. Multilevel logistic regression was used to calculate the OR (Odds Ratios) in racial gaps according to the primary outcomes with individuals at the first level and public health services at the second level.

RESULTS

The analytical sample comprised of 8993 respondents. Brown people were less likely to report better Access (27%), good reception services (31%), bond and responsibility (30%) and social participation (22%) than Whites. Black people showed similar patterns. Dental Specialities Centers that use health ombudsman for planning have attenuated racial inequities in all analysed dimensions.

CONCLUSIONS

Dental Specialities Centers that use active health ombudsman for planning showed lower racial inequities in access, reception, bond and responsibility and social participation than those who did not use. Therefore, the health ombudsman should be implemented and used for planning better specialized dental services in Brazil.

Psychometric Properties of the Bern Illegitimate Tasks Scale - Spanish Version

In recent years, a new factor for work stress has been studied along with stress as an offense to self-theory. Illegitimate tasks refer to assignments that are unnecessary or are not related to the employee’s role. Because of this, the Bern Illegitimate Tasks Scale was developed, which measures illegitimate tasks in terms of unreasonable tasks and unnecessary tasks. There are no studies in Latin America on illegitimate tasks, so the purpose of this research is to translate and validate the Bern Illegitimate Tasks Scale. The study was performed with a sample of nursing staff from a hospital in Guayaquil, Ecuador. Written informed consent was obtained from each of the participants. The reliability of the questionnaire was evaluated and its structural validity was verified by exploratory factor analysis and confirmatory factor analysis. The internal consistency of the whole scale, measured by Cronbach’s alpha, was 0.857. Moreover, the unnecessary and unreasonable subscales measure were 0.846 and 0.841, respectively. The exploratory factor analysis supported a two-factor model that explained 73.96% of the variance. Additionally, the confirmatory factor analysis showed good indexes of fit (GFI = 0.915, CFI = 0.955, TLI = 0.933, SRMR = 0.084, and RMSEA = 0.087). The Spanish version of the Bern Illegitimate Tasks Scale presents good psychometric properties and can be applied to nurses in the Ecuadorian population.