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Introduction: Past studies examined factors associated with rural practice, but none employed newer machine learning (ML) methods to explore potential predictors. The primary aim of this study was to identify factors related to practice in a rural area. Secondary aims were to capture a more precise understanding of the demographic characteristics of the healthcare professions workforce in Utah (USA) and to assess the viability of ML as a predictive tool. Methods: This study incorporated four datasets - the 2017 dental workforce, the 2016 physician workforce, the 2014 nursing workforce and the 2017 pharmacy workforce - collected by the Utah Medical Education Council. Supervised ML techniques were used to identify factors associated with practice location, the outcome variable of interest. Results: The study sample consisted of 11 259 healthcare professionals with an average age of 46.6 years, of which 36.6% were males and 94.5% Caucasian. Four ML methods were applied to assess model performance by comparing accuracy, sensitivity, specificity and area under the receiver operating characteristic (ROC) curve. Of the methods used, support vector machine performed the best (accuracy 99.7%, precision 100%, sensitivity 100%, specificity 99.4% and ROC 0.997). The models identified income and rural upbringing as the top factors associated with rural practice. Conclusion: By far, income emerged as the most important factor associated with rural practice, suggesting that attractive income offers might help rural communities address health professional shortages. Rural upbringing was the next most important predictive factor, validating and updating earlier research. The performance of the ML algorithms suggests their usefulness as a tool to model other databases for individualized prediction.

This study used a national database to update and examine current differences in men's and women's oral health and oral health behaviours in the United States.Data from the National Health and Nutrition Examination Survey for the 2017-2018 cycle were used to explore the relationship between males and females and their oral health. Multivariate analyses assessed for gender differences in oral health behaviors between genders after controlling for sample demographic characteristics.The final sample consisted of 4,741 participants. Males tended to have fewer dental visits, worse perception of their gum and tooth health, poorer flossing habits, and more root caries. Females were more proactive in visiting dentists and displayed a greater awareness of oral health. Females were less likely to report discussing oral cancer screening with their dentist even though they were screened more often. On examination, males were more often advised to seek urgent dental care than females. All these differences were statistically significant at p0.05, although the effect size for examination variables was small (Phi0.1).Oral health and oral health behaviours demonstrate gender differences with men reporting poorer oral health, poorer oral hygiene habits, and fewer dental visits. These findings suggest gender-targeted strategies have the potential to improve oral health and reduce gender-related disparities.This study found that women exhibit better oral health practices and behaviours. These differences may cause a disproportionate burden of oral disease in men and highlight the need for dentists, hygienists, and those interested in dental public health to develop gender-specific strategies to address these inequalities.

Background Despite controversy over their possible health consequences, manufacturers of e-cigarettes employ a variety of marketing media to increase their popularity among adolescents. This study analyzed the relationship between adolescent e-cigarette harm perception and five types of e-cigarette advertising exposures: social media, radio, billboard, newspaper, and television. Methods This study used data from Wave 4.5 of the Population Assessment of Tobacco and Health Study (PATH). PATH collects demographic data and interview individuals about issues pertaining to tobacco use, health outcomes, attitudes, and behaviors. This study applied factor analysis to three individual PATH harm perception items to develop a composite harm perception score. Using linear regression, the study explored the relationship of harm perception and participant responses to their recalled viewing of five different types (i.e., newspaper, radio, billboard, television and social media) of advertisements within the past 30 days. A second analysis explored if adjusting for exposure to anti-tobacco messaging and environmental factors such as family approval mitigated the association of harm perception and advertisement types. Results The study sample consisted of 12,570 (weighted N = 23,993,149) individuals aged 12 to 17 years old. Unadjusted past 30-day exposure to newspaper, radio, billboard, and social media advertising all correlated with a reduced harm perception, but only the associations for newspaper and social media were statistically significant (pp Conclusion E-cigarette advertising influences adolescent perceptions of harm in e-cigarette use, particularly for social media and newspaper advertisements. This association weakens when adjusted for covariates such as environmental support and exposure to anti-tobacco marketing. These findings provide evidence for policy makers to continue anti-tobacco marketing and incorporate environmentally supportive strategies such as holistic, family-centered educational approaches to reduce e-cigarette use among adolescents.

Objective This study sought to utilise machine learning methods in artificial intelligence to select the most relevant variables in classifying the presence and absence of root caries and to evaluate the model performance. Background Dental caries is one of the most prevalent oral health problems. Artificial intelligence can be used to develop models for identification of root caries risk and to gain valuable insights, but it has not been applied in dentistry. Accurately identifying root caries may guide treatment decisions, leading to better oral health outcomes. Methods Data were obtained from the 2015-2016 National Health and Nutrition Examination Survey and were randomly divided into training and test sets. Several supervised machine learning methods were applied to construct a tool that was capable of classifying variables into the presence and absence of root caries. Accuracy, sensitivity, specificity and area under the receiver operating curve were computed. Results Of the machine learning algorithms developed, support vector machine demonstrated the best performance with an accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6% and specificity of 94.3% for identifying root caries. The area under the curve was 0.997. Age was the feature most strongly associated with root caries. Conclusion The machine learning algorithms developed in this study perform well and allow for clinical implementation and utilisation by dental and nondental professionals. Clinicians are encouraged to adopt the algorithms from this study for early intervention and treatment of root caries for the ageing population of the United States, and for attaining precision dental medicine.

Purpose/objectives The coronavirus disease 2019 (COVID-19) pandemic arguably represents the worst public health crisis of the 21st century. However, no empirical study currently exists in the literature that examines the impact of the COVID-19 pandemic on dental education. This study evaluated the impact of COVID-19 on dental education and dental students' experience. Methods An anonymous online survey was administrated to professional dental students that focused on their experiences related to COVID-19. The survey included questions about student demographics, protocols for school reopening and student perceptions of institutional responses, student concerns, and psychological impacts. Results Among the 145 respondents, 92.4% were pre-doctoral dental students and 7.6% were orthodontic residents; 48.2% were female and 12.6% students lived alone during the school closure due to the pandemic. Students' age ranged from 23 to 39 years. Younger students expressed more concerns about their emotional health (P = 0.01). In terms of the school's overall response to COVID-19, 73.1% students thought it was effective. The majority (83%) of students believed that social distancing in school can minimize the development of COVID-19. In general, students felt that clinical education suffered after transitioning to online but responded more positively about adjustments to other online curricular components. Conclusions The COVID-19 pandemic significantly impacted dental education. Our findings indicate that students are experiencing increased levels of stress and feel their clinical education has suffered. Most students appear comfortable with technology adaptations for didactic curriculum and favor masks, social distancing, and liberal use of sanitizers.

The influence of familial and social environments plays a significant role in Electronic Nicotine Delivery System (ENDS) use and may contribute to poor oral health among adolescents. This study utilized the Population Assessment of Tobacco and Health (PATH) database and included youths aged 12 to 17 years who reported no history of dental health issues at baseline. Adjusted odds ratios (AOR) were used to examine the association between END-related familial factors and oral health among adolescents in the United States, with statistical significance set at p < 0.05. The sample consisted of 3892 adolescents (weighted N = 22,689,793). Parents’ extremely negative reaction towards ENDS when they found their children using ENDS (AOR = 0.309) was connected to a lower risk of oral health issues. The findings suggest that clinicians and policymakers need to consider the roles of these factors when developing strategies to improve oral health outcomes.

Background: Investigating the responsiveness of an instrument is important in order to provide meaningful interpretation of clinical outcomes. This study examined the responsiveness of the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function (PF), the PROMIS Pain Interference (PI), and the Foot and Ankle Ability Measure (FAAM) Sports subscale in an orthopedic sample with foot and ankle ailments. Methods: Patients presenting to an orthopedic foot and ankle clinic during the years 2014–2017 responded to the PROMIS and FAAM instruments prior to their clinical appointments. The responsiveness of the PROMIS PF v1.2, PROMIS PI v1.1, and FAAM Sports were assessed using paired samples t test, effect size (ES), and standardized response mean (SRM) at 4 different follow-up points. A total of 785 patients with an average age of 52 years (SD = 17) were included. Results: The PROMIS PF had ESs of 0.95 to 1.22 across the 4 time points (3, >3, 6, and Conclusion: The PROMIS PF, PROMIS PI, and FAAM Sports were sensitive and responsive to changes in patient-reported health. Level of Evidence: Level II, prospective comparative study.

This review of U.S. dental schools' clinical curricula suggests that the basic structure of clinical education has not changed significantly in the past 60 years, although important developments include the introduction of competency-based education and community-based clinical education. Most dental schools still have a two-year preclinical curriculum and a two-year clinical curriculum, and most schools still operate a large clinical facility where students receive the bulk of their clinical education and assessment for graduation. In those clinics, dental students are the main providers of patient treatment, with faculty serving in supervisory roles. In addition, a major portion of the entire dental curriculum continues to be dedicated to student education on the restoration of a single tooth or replacement of teeth. This article was written as part of the project "Advancing Dental Education in the 21st Century."

IntroductionAs total health and dental care expenditures in the United States continue to rise, healthcare disparities for low to middle-income Americans creates an imperative to analyze existing expenditures. This study examined health and dental care expenditures in the United States from 1996 to 2016 and explored trends in spending across various population subgroups.MethodsUsing data collected by the Medical Expenditure Panel Survey, this study examined health and dental care expenditures in the United States from 1996 to 2016. Trends in spending were displayed graphically and spending across subgroups examined. All expenditures were adjusted for inflation or deflation to the 2016 dollar.ResultsBoth total health and dental expenditures increased between 1996 and 2016 with total healthcare expenditures increasing from $838.33 billion in 1996 to $1.62 trillion in 2016, a 1.9-fold increase. Despite an overall increase, total expenditures slowed between 2004 and 2012 with the exception of the older adult population. Over the study period, expenditures increased across all groups with the greatest increases seen in older adult health and dental care. The per capita geriatric dental care expenditure increased 59% while the per capita geriatric healthcare expenditure increased 50% across the two decades. For the overall US population, the per capita dental care expenditure increased 27% while the per capita healthcare expenditure increased 60% over the two decades. All groups except the uninsured experienced increased dental care expenditure over the study period.ConclusionsHealthcare spending is not inherently bad since it brings benefits while exacting costs. Our findings indicate that while there were increases in both health and dental care expenditures from 1996 to 2016, these increases were non-uniform both across population subgroups and time. Further research to understand these trends in detail will be helpful to develop strategies to address health and dental care disparities and to maximize resource utilization.

Resource mismanagement along with the underutilization of dental care has led to serious health and economic consequences. Artificial intelligence was applied to a national health database to develop recommendations for dental care. The data were obtained from the 2013–2014 National Health and Nutrition Examination Survey to perform machine learning. Feature selection was done using LASSO in R to determine the best regression model. Prediction models were developed using several supervised machine learning algorithms, including logistic regression, support vector machine, random forest, and classification and regression tree. Feature selection by LASSO along with the inclusion of additional clinically relevant variables identified 8 top features associated with recommendation for dental care. The top 3 features include gum health, number of prescription medications taken, and race. Gum health shows a significantly higher relative importance compared to other features. Demographics, healthcare access, and general health variables were identified as top features related to receiving additional dental care, consistent with prior research. Practicing dentists and other healthcare professionals can follow this model to enable precision dentistry through the incorporation of our algorithms into computerized screening tool or decision tree diagram to achieve more efficient and personalized preventive strategies and treatment protocols in dental care.

Aims Little evidence exists to confirm that better oral health is associated with better overall health and well-being. The present study aimed to examine the impact of oral health on the overall health of the population greater than 65-year old in the entire United States. Methods and results Data from National Health and Nutrition Examination Survey (NHANES) 2015-2016 were used. Variables included demographics and perceptions of oral health and overall health and well-being. Weighted prevalence estimates were calculated using mean, standard deviation, and percentage as appropriate. Chi-square tests and logistic regressions were performed to examine the association of oral health with physical health, mental health, general health, and systemic disease conditions. Analyses showed statistically significant relationships between oral health, physical, mental and general health, energy levels, work limitation, depression, and appetite. Out of the 10 systemic diseases being investigated, six of them were directly related to oral health outcome. Conclusion This study provided strong empirical evidence that oral health is directly associated with different disease conditions and contributes largely to an individual's general health, particularly in the elderly. In the current landscape of patient-centered and value-based care, addressing the oral health needs of the elderly, who generally find themselves with limited access to care, should be a priority.

The goals of this study were to develop a risk prediction model in unmet dental care needs and to explore the intersection between social determinants of health and unmet dental care needs in the United States. Data from the 2016 Medical Expenditure Panel Survey were used for this study. A chi-squared test was used to examine the difference in social determinants of health between those with and without unmet dental needs. Machine learning was used to determine top predictors of unmet dental care needs and to build a risk prediction model to identify those with unmet dental care needs. Age was the most important predictor of unmet dental care needs. Other important predictors included income, family size, educational level, unmet medical needs, and emergency room visit charges. The risk prediction model of unmet dental care needs attained an accuracy of 82.6%, sensitivity of 77.8%, specificity of 87.4%, precision of 82.9%, and area under the curve of 0.918. Social determinants of health have a strong relationship with unmet dental care needs. The application of deep learning in artificial intelligence represents a significant innovation in dentistry and enables a major advancement in our understanding of unmet dental care needs on an individual level that has never been done before. This study presents promising findings and the results are expected to be useful in risk assessment of unmet dental care needs and can guide targeted intervention in the general population of the United States.

Background: Establishing score points that reflect meaningful change from the patient perspective is important for interpreting patient-reported outcomes. This study estimated the minimum clinically important difference (MCID) values of 2 Patient-Reported Outcomes Measurement Information System (PROMIS) instruments and the Foot and Ankle Ability Measure (FAAM) Sports subscale within a foot and ankle orthopedic population. Methods: Patients seen for foot and ankle conditions at an orthopedic clinic were administered the PROMIS Physical Function (PF) v1.2, the PROMIS Pain Interference (PI) v1.1, and the FAAM Sports at baseline and all follow-up visits. MCID estimation was conducted using anchor-based and distribution-based methods. Results: A total of 3069 patients, mean age of 51 years (range = 18-94), were included. The MCIDs for the PROMIS PF ranged from approximately 3 to 30 points (median = 11.3) depending on the methods being used. The MCIDs ranged from 3 to 25 points (median = 8.9) for the PROMIS PI, and from 9 to 77 points (median = 32.5) for the FAAM Sports. Conclusions: This study established a range of MCIDs in the PROMIS PF, PROMIS PI, and FAAM Sports indicating meaningful change in patient condition. MCID values were consistent across follow-up periods, but were different across methods. Values below the 25th percentile of MCIDs may be useful for low-risk clinical decisions. Midrange values (eg, near the median) should be used for high stakes decisions in clinical practice (ie, surgery referrals). The MCID values within the interquartile range should be utilized for most decision making. Level of Evidence: Level I, diagnostic study, testing of previously developed diagnostic measure on consecutive patients with reference standard applied.

Almost 20 years ago dental education, including ancillary and residency training, made a fundamental shift to a competency model. Competency is the level of knowledge, skills, and values needed to begin independent practice. This replaced the older emphasis on process. It had formerly been assumed that if a student was exposed to good teaching for a set period of time, he or she must be ready for practice. The responsibility has been shifted from schools needing to demonstrate that they have done the traditional things well to requiring that they demonstrate that every graduate is in fact capable of independent performance as a dentist. This paper describes the nature of competency in predoctoral dental education and introduces some of the most common assessment methods schools use to ensure that each graduate is competent.

Dental therapy is an accepted component of the dental profession in the Alaska Native Tribal Health Consortium and in the state of Minnesota. There are also several states working to enact legislation to permit the practice of dental therapy. However, in the absence of nationally recognized educational standards, concerns have been raised relating to the lack of uniformity in dental therapy education. In 2010, a panel of academicians met and prepared a report on the principles, competencies, and curriculum for educating dental therapists. Still, there remained questions in regard to what the minimal educational standards should be for institutions that wish to sponsor dental therapy programs. A second panel was convened to address education standards. This paper describes the Panel's deliberations on three critical issues in developing the report: the length of the program and degree to be awarded; credentials of the program director; and the nature of supervision.

Surveys are the most common form of data-based article published in the Journal of Dental Education. The apparent ease with which they are conducted and the seeming simplicity of reporting results mask significant issues in sample design and performing maximally useful analyses. Four concerns are discussed here. First, it is demonstrated that results are a function of who, when, and where responses are sampled, each source making independent contributions. Second, absolute sample size is shown to be the most significant factor affecting precision in surveys, and the numbers of schools, respondents, and other sources of variance can be chosen to minimize survey imprecision. Third, response rate typically has negligible effect on precision and an uncertain effect on accuracy (freedom from bias). A technique, sample saturation, is explained that can be used to protect, to some degree, surveys from the effects of bias. Finally, suggestions are offered for reporting survey results in a visually meaningful fashion, and an appeal is made that recommendations associated with surveys not be published unless they are grounded in both data and well-developed theory. This analysis references a previously published survey on competency-based dental education to illustrate methodological points in concrete terms.

Competency-based dental education was introduced in 1993 and has proven to be a robust innovation, guiding curricular design, clinical education and evaluation, and accreditation. At the same time, it has been irregularly implemented and is understood in different ways. These paradoxes were explored in a survey of academic and clinical deans and chairs of departments of endodontics and restorative dentistry at U.S. and Canadian dental schools. It was confirmed that fewer than half of the respondents can identify the ADEA and ADA definition of competency. Significant differences were reported in the perceived understanding and value placed on competencies and their impact on dental education. Differences were also found to exist in evaluation practices and in how evaluation data are used to determine students' readiness for graduation. It is concluded that the openness of the competency concept is one reason for its longevity and usefulness in dental education.

For meaningful curriculum change to occur in dental schools, faculty must go through a process of new skills development that will prepare them to teach differently and to assess students differently than they have before. Curriculum change and the faculty development process must have the support of the dental school’s leadership and become a core value of the school’s culture.

The purpose of this article is to discuss how traditional dental school curricula are inconsistent with research in how learners learn. In the last ten years, there has been considerable discussion about the need for dental education reform, and innovative changes have occurred in the curricula of a number of U.S. dental schools. However, efforts in curriculum restructuring have been hindered by the lack of evidence that one specific curriculum design achieves outcomes superior to other designs. Moreover, there has been little discussion in the dental literature about how modern theories of learning can provide a sound rationale for change in dental education. Thus, it is important for those involved in curriculum reform to present the rationale for change based on the best available evidence. In this review, we summarize aspects of research on learning that seem applicable to dental education and outline ways in which curricula might be changed to become more consistent with the evidence.

In fall 2002 the University of Illinois at Chicago College of Dentistry implemented a Group Practice Comprehensive Care Clinical Education Curriculum. The primary responsibility for patient care has shifted in this comprehensive care curriculum from the students to the faculty and staff. Students have a primary responsibility for learning. This competency-based education curriculum utilizes a variety of student evaluation methods including self-evaluation, OSCE, and portfolio to verify competence. Formative evaluation methods are utilized in daily assessment of student performance. On-time graduation rates have increased from 60-70 percent to 96 percent, and regional board first-time pass rates have been maintained at 90+ percent. Overall predoctoral clinical productivity in the first full year of the program has increased by over 300,000 dollars.

In fall 2002, the ADEA Section on Comprehensive Care and General Dentistry conducted a survey of the predoctoral clinical curriculum models at sixty-four North American dental schools. Fifty-eight percent of the schools reported that most patient care is provided in a comprehensive care clinic setting, 22 percent reported that most patient care is provided in discipline-specific settings, and 20 percent reported a hybrid of comprehensive care and discipline-specific settings. While ten Primarily Discipline-Based (PD) schools have instituted new Primarily Comprehensive Care (PCC) or Hybrid clinical curricula since 1997, one PCC school has converted to a Hybrid model, and one PCC school has converted to a PD model. PCC curriculum models were frequently associated with the following institutional factors: more densely populated metropolitan areas; private institutional sponsorship; location within a university medical center; larger class size; and more students enrolled in advanced training at the school. Curriculum factors frequently associated with PCC models included the following: increased use of simulation technology: higher proportion of clinical/teaching track faculty; higher proportion of part-time faculty; higher proportion of generalist faculty; same faculty supervising both treatment planning and patient treatment; and use of competency exams as the main requirement for completion of the curriculum.

Surveys are the most common form of data-based article published in the Journal of Dental Education. The apparent ease with which they are conducted and the seeming simplicity of reporting results mask significant issues in sample design and performing maximally useful analyses. Four concerns are discussed here. First, it is demonstrated that results are a function of who, when, and where responses are sampled, each source making independent contributions. Second, absolute sample size is shown to be the most significant factor affecting precision in surveys, and the numbers of schools, respondents, and other sources of variance can be chosen to minimize survey imprecision. Third, response rate typically has negligible effect on precision and an uncertain effect on accuracy (freedom from bias). A technique, sample saturation, is explained that can be used to protect, to some degree, surveys from the effects of bias. Finally, suggestions are offered for reporting survey results in a visually meaningful fashion, and an appeal is made that recommendations associated with surveys not be published unless they are grounded in both data and well-developed theory. This analysis references a previously published survey on competency-based dental education to illustrate methodological points in concrete terms.

Most dental school instructors struggle to develop course evaluation criteria that can effectively be applied as valid and reliable learning instruments. Vague and unreliable learning assessments often lead to increased dissatisfaction among both faculty and students. Students complain about the "lack of faculty calibration," and faculty are often unable to adequately evaluate competence due to the need to provide an overall course grade by the end of the term. By systematically addressing Mackenzie et al.'s list of sixteen factors that contribute to faculty disagreements on student evaluation, we developed "Criteria for Writing Effective Evaluation Forms" as a guide for developing evaluation criteria. By using the guide for developing evaluation forms for student learning, course directors will have the components necessary to ensure validity and reliability of student assessment methodology. By providing students and faculty with clearly defined criteria and the training to apply those criteria, Mackenzie et al.'s concerns may be conquered.

Competency-based dental education was introduced in 1993 and has proven to be a robust innovation, guiding curricular design, clinical education and evaluation, and accreditation. At the same time, it has been irregularly implemented and is understood in different ways. These paradoxes were explored in a survey of academic and clinical deans and chairs of departments of endodontics and restorative dentistry at U.S. and Canadian dental schools. It was confirmed that fewer than half of the respondents can identify the ADEA and ADA definition of competency. Significant differences were reported in the perceived understanding and value placed on competencies and their impact on dental education. Differences were also found to exist in evaluation practices and in how evaluation data are used to determine students' readiness for graduation. It is concluded that the openness of the competency concept is one reason for its longevity and usefulness in dental education.

In fall 2002 the University of Illinois at Chicago College of Dentistry implemented a Group Practice Comprehensive Care Clinical Education Curriculum. The primary responsibility for patient care has shifted in this comprehensive care curriculum from the students to the faculty and staff. Students have a primary responsibility for learning. This competency-based education curriculum utilizes a variety of student evaluation methods including self-evaluation, OSCE, and portfolio to verify competence. Formative evaluation methods are utilized in daily assessment of student performance. On-time graduation rates have increased from 60-70 percent to 96 percent, and regional board first-time pass rates have been maintained at 90+ percent. Overall predoctoral clinical productivity in the first full year of the program has increased by over 300,000 dollars.

In fall 2002, the ADEA Section on Comprehensive Care and General Dentistry conducted a survey of the predoctoral clinical curriculum models at sixty-four North American dental schools. Fifty-eight percent of the schools reported that most patient care is provided in a comprehensive care clinic setting, 22 percent reported that most patient care is provided in discipline-specific settings, and 20 percent reported a hybrid of comprehensive care and discipline-specific settings. While ten Primarily Discipline-Based (PD) schools have instituted new Primarily Comprehensive Care (PCC) or Hybrid clinical curricula since 1997, one PCC school has converted to a Hybrid model, and one PCC school has converted to a PD model. PCC curriculum models were frequently associated with the following institutional factors: more densely populated metropolitan areas; private institutional sponsorship; location within a university medical center; larger class size; and more students enrolled in advanced training at the school. Curriculum factors frequently associated with PCC models included the following: increased use of simulation technology: higher proportion of clinical/teaching track faculty; higher proportion of part-time faculty; higher proportion of generalist faculty; same faculty supervising both treatment planning and patient treatment; and use of competency exams as the main requirement for completion of the curriculum.