community exam 2

History of water fluoridation
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History of water fluoridation
Dr. Frederick McKay in the 1920's noticed that people had mottled enamel and brown stains but no caries.

Then, in the 1930's, Dr. Trendley Dean did epidemiological studies to prove the relationship of dental fluorosis, concentration of fluoride in the water, and reduction of dental caries.

Grand Rapids, Michigan was the first U.S. city to adjust the fluoride content in community water. After this, in the 1950's and 1960's, many states and cities began to implement this.
How does warmer climates affect fluoride levels?
both the amount and the rate of fluoride uptake increased significantly with increase in temperature
What is fluoride varnish and when is it used:
An effective evidence-based approach to prevent caries in primary and permanent teeth of children and adults.

It is recommended that primary care medical providers apply fluoride varnish to children two to four times a year.
Fluorosis what causes it:
Early use of fluoride toothpaste.
Use and misuse of dietary supplements.
Consumption of infant formula containing fluoride.
Bottled water with fluoride.
Antifluoridation argument and how to affect a campaign
Their reasons include individual rights, safety, government mistrust, and religious freedom.

They provide inaccurate, false information to the public and to the elected officials and attempt to link adverse health effects with fluoridation.

Being aware of the issues and being well versed on fluoridation studies and cognizant of the political process are necessary steps in winning a fluoridation campaign.

Five basic components of a successful fluoridation campaign:
1. Understand the local context
2. Involve key players**
- Public
- Dental health professionals
- City support, etc...
3. Establish a strategy
4. Obtain necessary funding
5. Understand the opposition
San Antonio's natural level of fluoride?
0.3 ppm. SAWS then adds enough fluoride to bring the fluoride level up to 0.7 ppm.
Know how to estimate (calculate) how much fluoride should go into the school water supply
In 2015-2021, PHS recommended the level of fluoride in the water is 0.7 mg ppm.
Explain the importance of research in relation to dental hygiene practice.
Professional Development
Education
Regulation
Occupational health

Client Level
Basic Science
Oral health care

Population Level
Health services
Access to care
Describe evidence-based decision making (EBDM), explain the levels of evidence used for EBDM, and relate EBDM and the levels of evidence to research.
Evidence pyramid is arranged so that the types of literature at the top are the most reliable for aiding in clinical decision making

Primary Literature-Original reports of new information, representing original thinking and reporting a discovery
- All other studies

Secondary Literature-Interpretations and evaluations of primary sources
- Systemic reviews

Tertiary literature-Summarizes primary and secondary sources

The gold standard of evidence (best clinical evidence available) is at least one published systematic review of multiple, well-designed studies of the type that is best to answer the research question.

Narrative reviews are at the bottom of the pyramid and systematic reviews especially those with a meta-analysis have the highest ranked evidence for EBDM.
Image: Describe evidence-based decision making (EBDM), explain the levels of evidence used for EBDM, and relate EBDM and the levels of evidence to research.
Explain the importance and discuss the steps in the scientific method in researching questions related to dental hygiene practice.
- Be able to apply "fluoride case study" to steps

1. Formulating a problem
2. Doing background research
3. Developing hypothesis
4. Collecting the data
5. Analyzing the results
6. Drawing a conclusion
7. Communicating the results
Differentiate between the research hypothesis and the null hypothesis of a research study.Hypothesis- A statement that reflects the research question. It's stated in positive terms that represent the researcher's prediction or opinion. Null Hypothesis- The research statement that assumes that there is no statistically significant difference between the groups being studied. After the hypothesis has been formed, data can be collected to prove or disprove the statement. It's easier to DISPROVE something *(the null hypothesis)* rather than to PROVE something *(the alternative hypothesis)*, so research studies are designed to disprove (or "reject") the null hypothesis. The *p-value* is used to reject the null hypothesis.Development of a hypothesis - Example • You want to know if a new toothpaste, SuperSoothe, is effective at relieving dentin hypersensitivity (DHS). - state the null hypothesis and alternative hypothesis - if p<0.05, we can say that SuperSooth...• In this case the null hypothesis would be that there is NO difference between SuperSoothe and standard toothpaste in relieving DHS symptoms. • The alternative hypothesis is that there is a difference between the two pastes in relieving DHS symptoms. • The p-value is a statistic that is used to reject the null hypothesis. In this case, if p<0.05, we can say that SuperSoothe is more effective at relieving DHS.Qualitative(language) • Methods rely on language to answer the question. • Data = words Understand, describe, discover, give meaning, generate hypotheses Can be collected via documented narratives, interviews, documented observations, focus groups, or manuscripts.Quantitative(numbers) • Methods rely on numbers to answer the question. - Test hypotheses, determine cause and effect, and make predictions. - Can be collected using a clinical examination, survey, observation, or patients' charts.Which Design do you choose? - burden of disease? - Field surveys? - treatment effect? - Diagnostic test evaluation? - Cost effectiveness? - Prognosis? Question types: - diagnosis - therapy - prognosis - harm/etiology - prevention- prevalence, incidence - cross sectional survey, cohort longitudinal survey - RCT - RCT - RCT - Cohort - prospective, blind comparison to a gold standard - RCT>cohort>case-control>case series - Cohort study>case-control>case series - RCT>cohort>case-control>case series - RCT> cohort>case-control>case seriesList the types of epidemiological studies under (descriptive and analytic) observation population-based vs. individual based and experimental randomized vs. non-randomized.observation population based - descriptive: health survey - analytic: ecological study Individual based - descriptive: case reports/series - analytic: ecological study experiment - randomized: RCT - non-randomized: quazi-experimental, field trial, community trialExperimental vs. observational studies*• Observational studies* • Researcher measures the effect of a risk factor ("exposure") on an outcome • No treatment is assigned or manipulated. • Purpose - to identify risk factors for, and causes of, disease • 4 types: descriptive, cross-sectional, cohort (prospective) & case control (retrospective) studies *• Experimental studies* • Researcher provides a treatment (intervention) to some subjects but not to others ("controls") • Purpose - to study the effect of a treatment on an outcome • Randomized controlled trials (RCTs) & other types of clinical trialstypes of observational studies*• Descriptive studies* (case reports) • Define characteristics (disease/condition) of a population or sample • Provides insight into a problem *• Cross-sectional studies* • Gives info on prevalence of a condition among those exposed *• Retrospective* (case control studies) • Study looks backward to identify prior exposures in relation to an outcome *• Prospective* (cohort studies) • Study looks at outcomes forward in time, and how the outcome is related to risk factorsExperimental research - key features- Allows investigators to assess *cause-and-effect* of a variable of interest, rather than simply a correlation or association - Has *stringent selection criteria* to ensure that subjects are comparable in most respects isolates the effect of the intervention being tested - *Randomization* ensures that any prognostic factors are equally divided between groups - Furthermore, *blinding reduces bias* the likelihood that behaviors of subjects or investigators could influence the results of the studyExperimental research designs common to oral health - List the five types and define*Pretest-posttest design*- data is collected at baseline and after the treatment intervention. - Dependent variable is measured before (baseline) and after the treatment intervention is introduced. - Pretest-posttest designs are employed in both experimental and quasi-experimental research and can be used with or without control groups - For example, quasi-experimental pretest-posttest designs may or may not include control groups, whereas experimental pretest-posttest designs must include control groups. *Repeated measures design*- longitudinal research; data is collected at baseline and at multiple points over time. *Crossover design*- Each patient serves as their own 'control' which helps to control any differences between experimental and control group members in that both groups are made up of the same people. • Washout period is used to minimize risk of 'carryover effect' from first period *Split-plot (split-mouth) design*- Each patient serves as their own 'control'. The mouth of an individual is divided into two or more experimental segments that are randomly assigned to different treatments. *Factorial design*- Used when the researcher is interested in studying two or more independent variables/interventions. Simultaneous assessment of multiple factors. • Multiple groups X multiple factors - simultaneous assessment of multiple factors. - Completed with two-way ANOVA.Quasi-experimental research• Similar to experimental design • Participants are not randomized • Example • Community trial • Used to evaluate policies, programs, or preventive treatments at the community levelResearch methodology - key aspects- validity, reliability and bias - data - population and sampling - experimental and control groups and variablesWhat is 'validity'?*Internal validity*- the integrity of the study itself or how well the study is conducted without bias. Is the study designed in a way that we can trust the findings? *External validity*- Examines whether the study findings can be generalized to the population at large. Is the study designed in a way that we can generalize the findings to our patients?Reliability*Reliability*- refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable. *Intra-rater reliability*- the degree of agreement among multiple repetitions of a diagnostic test performed by a single rater. Ex: Weighing yourself over the course of a day on different scales. *Inter-rater reliability*- the degree of agreement among multiple raters. It gives a score of how much consensus, there is in the ratings given by several examiners. Ex: Clinic Phase Exam Calibration- is important for examiners to be calibrated!What is 'bias'?*• Systematic errors* that influence or "trend" the outcome of a study *• Non-random* (i.e., directional) deviations from truth • Affects the comparison between groups • Leads to overestimation or underestimation of the true results • Bias can occur at any point of a study: • study design • conduct of the study • data analysis • reporting of study resultsPopulation and sampling*• Population* = the entire group or whole unit of individuals having similar characteristics from which the results can be inferred. EX: Adult Diabetics; Breast Cancer Survivors • The term *parameter* is used to describe numeric characteristics of the population. Measured by: Population mean and standard deviation • Ex: The average height of adult women in the United States *• Target population* = the population from whom the information is being collected *• Sample* = a portion or subset of the entire population that, if properly selected, can provide meaningful information about the entire population. • The term *statistic* is used when discussing numeric characteristics of samples. • Sample mean and standard deviation • The average height of adult women enrolled in your research study can be described by such statisticsSimple random sampling• You systematically compile a complete list of all people or items that exist in the population. • You then randomly select subjects from that list and include them in the sample. • Provides the most external validity, or degree to which the results of the study can be generalized to settings other than the one included • Each member of a population has an equal chance of being included; prevents the possibility of selection bias by the researcher. • Ex: Selecting names out of a hat or jarstratified random sampling• Subdivisions of a population with similar characteristics are called strata (by gender, race, age, disease category, occupation, etc.) • The random selection of subjects from two or more strata of the population is another way of defining stratified sampling.Systematic sampling• Sample members chosen from a larger population are selected according to a random starting point and a fixed, periodic interval • Involves the selection of subjects by including every nth person in a list • Unless the list is in random order, not every person may have an equal or random chance of being selected; thus, systematic sampling may not be considered a true random sample. - Calculated: calculated by dividing the population size by the desired sample size - Population size N, desired sample size n, sampling interval k=N/n. Ex: N=64, n=8, k=64/8 = 8 - We select 8 randomly from a list of 64 to participatePurposive (judgmental) sampling• A non-probability (non-random) method • Provides a sample, through personal judgment, of subjects who would be most representative of the population • Advantages: Flexible, facilitates purpose of study, simple • Disadvantages: Bias, not representative of populationconvenience sampling- Provides a group of individuals who are most readily available to be subjects in the study. Ex: Classmates, friends, familyExplain variables: compare and contrast the independent and dependent variables; explain the significance and relationship of relevant and extraneous variables.- A variable is a characteristic or concept that varies within the population under study. - Independent variable- The variable that is manipulated by the researcher and is believed to cause or influence the dependent variable. The intervention that is imposed on the experimental group of an experimental or quasi-experimental study. - Dependent variable- The variable that is thought to depend on or to be caused by the independent variable. Is always measured during the course of an experimental study. - Relevant variable- Any variable that should be controlled because it can influence how the independent variable affects the dependent variable; relevant variables vary for the condition being studied. - Extraneous variable (confounding variable)- Variables that can influence (confound) the relationship between the independent and dependent variables and potentially be sources of error in relation to any observed effects in the study outcomes. Is not controlled in a study. - Reduces the internal validity (the experimental treatment or independent variable that is responsible for the observed effects) of an experimental study.Blinding/masking• Blinding reduces bias • The likelihood that behaviors of study participants or investigators could influence the results of the study *• Single-blind study* • Study participants or examiners are unaware of the group assignment. *• Double-blind study* • Study participants, as well as the researchers and examiners, are unaware of the study.Length of time• Appropriate length of time of a study depends on the variables being studied and the type of study. • Study must be long enough to allow detection of new disease and extension of current disease. • General recommendations: - plaque (8-21 days) - calculus (90 days) - gingivitis (6 months) - caries (2-3 years)Categorical and dichotomous data*• Categorical data:* • Gender, ethnicity, eye color, hair color, etc. • No category is superior to another *• Dichotomous data* - a subset of categorical data • Only two possibilities exist • Caries/no caries • Implant survival/failureDiscrete v. Continuous data*• Discrete data:* limited set of values, whole numbers (e.g., number of patients treated) *• Continuous data:* any value within a given range • Can be meaningfully subdivided into finer and finer increments, depending upon the precision of the measurement system • Height, weight, periodontal probing depth, etc.Data: Scales of Measurement- *Nominal scale*- consists of named, mutually exclusive categories that have no order. Examples= females in one category of gender, and males in another. Ethinic group members, religious preference. The nominal scale is the *simplest or least complex* of the four scales of data. - *Ordinal scale*- consists of categories of variables that have *rank order*, but there is no equal or defined value between the ranks. Example= cancer staging for tumors, periodontal disease stages, socioeconomic status. Many dental indexes are ordinal scale - *Interval scale*- has an equal distance between measures along the continuum, but there is *no true zero point*, meaning there is *no absence of the variable.* Examples= temperature, IQ scale, and time on a clock. *Oral health variables are not typically interval scale* - Ratio scale- has equal intervals between the measures along a continuum, and there is a meaningful *absolute zero point* determined by nature, meaning there can be *absence of the variable* being measured. Examples= height and weight, number of teeth or sealants, blood pressure. *MOST COMPLEX* • Discrete data can use nominal or ordinal scales of measurement. • Continuous data use interval and ratio scales or measurements.Rank types of data scales from least to most powerful.Least to most powerful 1. Nominal 2. Ordinal 3. Interval 4. Ratiopurpose of statistical analysisto make an inference or an assumption about a population.Two types of statistics1. Descriptive - Purpose: Present, organize, and summarize data from the study sample 2. Inferential - Purpose: Draw conclusions about a population based on data observed in a sampleMeasures of central tendency: Mean, median & mode- *Mean* is generally considered the best measure of central tendency and the most frequently used - *Median* is preferred to mean when there are few extreme scores (outliers) in the distribution - *Mode* is the preferred measure when data are measured in a nominal (name) scaledispersion: Variance expressed as Standard Deviation (SD)• These measures tell us how far the scores in a dataset differ from the mean. • *Range* is the difference between the highest and lowest values in a dataset. • *Standard deviation* is a measure of the spread of observed values (or "variance") in relation to the mean • It is the square root of the variance • *Variance* is the average deviation from the mean, calculated by these steps: • Subtract the mean from each value in the dataset • Square each of these numbers • Calculate the average of these numbersIf examining two variables in a research study, you would hope to get a minimal acceptable correlation of at least _____0.70If a research study was quoting the "p" values used, which would be more powerful or significant, p=0.03 or p=0.05p=0.03A parameter is data from a population, just as a _______ is data from a sample.statisticAn inferential statistical technique hopes to do what?Used to apply info from a sample to a larger population * to generalizeCorrelation (r)• A statistical measure that expresses the extent to which two variables are *linearly related* • linearly related = they change together at a constant rate • A common tool for describing simple relationships • The *value for r communicates* : • the *direction* (+ or -) of the association between two variables • the *strength* (-1.0 to 1.0) of the association between two variables • Correlation does *NOT* tell us about cause and effect • Pearson Product-most common correlation coefficient used • When r is *positive:* • For a positive increase in one variable, there is also a positive increase in the second variable. • A value of exactly +1.0 means there is a perfect positive relationship between the two variables. • When r is *negative:* • This shows that the variables move in opposite directions; for a positive increase in one variable, there is a decrease in the second variable. • A value of -1.0 means there is a perfect negative relationship between the two variables. • If the correlation between two variables is 0, there is *no linear relationship* between them • When plotted, the closer the data points come to making a straight line, the higher the correlation between the two variables, or the stronger the relationship.Positive, negative, no correlation graphsPercentiles• Statistical measure that represents the value below which a specific percentage of observations fall in a distribution of values • Often used to report scores on a norm- referenced test (boards, SAT) • Example: test score is in the 90th percentile, it is higher than 90% of the other test scores.Frequency distribution table• Raw data are put in order; the frequency of each value is calculated. • Frequencies can be expressed as an actual frequency, counts, or as a relative frequency or percentage of those surveyed • Ex: # Pet Research HomeworkBar graph:Used to display nominal or ordinal data that are discrete in nature - space between barsHistogram• a type of bar graph; used most often to represent interval or ratio scaled variables that are continuous in nature. - no space between barsFrequency polygon:• used to represent data that are continuous in nature.Time series graph• Usually presented as line graphs showing a variable over time• Good for comparing two groups in a graphical/visual mannerPie chart• Circular graphic that illustrates numeric proportion by dividing the whole circle or pie into sectionsInferential statistics• Used to *infer information* from the sample to a larger population • You are testing a hypothesis and drawing a conclusion based on sample *• ANOVA, T-Test, Chi-Squared, confidence interval, regression, etc* • Use of inferential statistics may include *parametric* (t-test, ANOVA) or *nonparametric* (Chi-square) statistical techniquesNormal distributions• A normal distribution assumes that: • approximately *68%* of the population fall within *one SD* of the mean. • Approximately *95%* fall within *two SDs* of the mean. • *99%* lie within three SDs of the mean. • Commonly called Gaussian curve • Data forms a bell-shaped curve defined by the standard deviation (σ) and the mean (μ) • This curve is symmetrical about the mean • Mean, median and mode are coincident • Asymmetrical (skewed) distributions are not normally distributedparametric tests• Use continuous data • quantitative in nature • Assumes *random sampling and normal statistical distribution* • These tests have more statistical power than their nonparametric counterparts • They are *more able to lead to a rejection of the null hypothesis* • p-values associated with a parametric test will often be smaller than the p-values associated with their nonparametric counterpartsNonparametric tests• *Use nominal or ordinal data* • qualitative nature • Doesn't assume normal distribution • Compares sample means in a *skewed distribution* • Can be used when the *sample is small* • Are valid in a broader range of situations • More conservative tests; not as statistically "strong" as parametric testsPaired t-test- used when you have *two matched* related observations. Goal is to see if results are *repeatable.* - Tells you if the differences measured in means between two matched groups could happen by chance. - Ex: Drug company wants to compare average life expectancy over time with a new drug Group A (breast cancer) - New therapy drug (intervention) Group B (breast cancer) - Control Group (placebo)t-test- Determines differences in the mean between two independent groups such as an experimental and control group. - Test for significance of correlation/regression coefficients using a single mean - Does the single group of subject differ from known reference value - Single group compared before/after treatment - Assumes that the independent variable consist of *two categorical, independent groups* (juniors vs. seniors; male vs. females; test vs. control) - *Compares equality of mean values between two groups* Ex: Junior's text anxiety vs. Senior's test anxietyAnalysis of Variance (ANOVA)• *Used to analyze the difference between the means of more than two groups* (i.e., 3 or more sample means) • Compares differences within groups (within each brand of toothpaste, for instance) and between groups (between different brands)one-way ANOVAwhen you have collected data about one categorical independent variable and one quantitative dependent variable. • Example: you want to know which wire type (one categorical independent variable) generates the greatest force for tooth movement (quantitative dependent variable)two-way ANOVA- when you want to know how two independent variables, in combination, affect a dependent variable. • Example: you want to know which combination of wire type and bracket type (two independent categorical variables) generate the greatest force for tooth movement (quantitative dependent variable)Chi-square test (nonparametric distributions of data)*• The most used nonparametric test - sometimes called the 'goodness of fit' test* • Compares observed to expected frequencies of 2 or more independent groups. • Useful for data measured on a nominal or ordinal scale (gender, ethnicity, treatment/control, Likert scale) • This test may be used to analyze questionnaire data and to determine whether a relationship exists between two variables. • Ex: You want to know how well boys vs. girls like using a powered toothbrushConfidence intervals- An inferential statistic by which researchers estimate the *accuracy of a sample statistic* such as the sample mean. - Provides a range of values which is likely to contain the population parameter of interest. - *Consists of two parts* 1. Interval (range of values) 2. Percentage level of confidence (usu. 95%) - A 95% confidence interval is a range of values that you can be 95% certain contains the true mean of the population - Smaller samples mean wider confidence intervalsDetermining Statistical Significance• Statistical tests provide researchers with an idea of what the data they have collected say about the sample and perhaps what the data imply about the population from which the sample was drawn. • Statistical significance is a *way of indicating that the results found in the analysis of data are unlikely to have been caused by chance.* • More likely, the results have been caused by the independent variable.Power Analysis*Determining how many subjects are needed to provide significance is called a power analysis.* *• It is calculated according to a specific statistical formula based on what the researcher hopes to observe in most of the subjects.* • The power of a study, or its ability to detect relationships among variables, is directly related to sample size, the definition of the independent variable, and the precision with which the study is planned and conducted. • When too large a sample is used, the effects may be statistically significant but clinically of no consequence. • The greater the significance, the more statistical inference can be made regarding the population from which the sample was taken. • A major issue with regard to statistical inferences is that every measurement taken from the sample being researched has some degree of error. • Researchers may describe the possibility of error or lack of error in various ways.p Value- The p value states how likely it is that the study could have come to a false scientific conclusion. • p values are calculated according to the sample size, the difference between the means of the control and experimental group, and the SD of the distribution. *- The smaller the p value, the more significant the findings of the study.* - Low p value = strong evidence against null hypothesis - A normally acceptable p value is p < .05 • Results with a p value of less than .05 are generally considered *statistically significant* and provide the basis for rejection of the null hypothesis. - p < .05 means that the results were due to chance only 5 times in 100. • p values of approximately .01, .001, and lower increase the significance of the study.Statistical Conclusion α False Positive β False Negatives• Occasionally, when formulating a conclusion, a researcher may make an error *• Type I alpha (α)* errors occur when, according to statistical results, the researcher rejects the null hypothesis when it is true • The researcher's conclusion states that the α relationship exists when it does not *• Type II beta (β)* errors occur when the null hypothesis is accepted but is actually false • The conclusion states that no relationship exists when one actually doesSix theories of health promotion and their componentsIntrapersonal Level 1. Stages of Change Theory (transtheoretical model)- view changes as a process or cycle over time not a single event. - Dental Hygienist use 2. Health Belief Model- Specific perceptions are necessary to motivate an individual to take preventive action. - Susceptible to disease or condition - Disease or condition is serious - Recommended action will prevent the disease or condition - Cost or negatives of preventive action are outweighed by benefits Interpersonal Level 3. Social Learning Theory (aka Social Cognitive Theory)- People learn by observing the actions of others. The way they cognitive process the information or environment influences their behavior. - Vicarious learning (Oversrvational learning) - Remembering and imitation the behavior (modeling) - Inferences from the evidence of observed outcomes from behavior. - Motivation from judgment voice (testimonies, or promotions by experts) Community Level 4. Community Organization Theory- Process of involving and activating community members or subgroups. - Identify a common problem or goal - Identify and mobilize resources to address the problem - Implement the chosen strategies - Evaluate their effort 5. Diffusion of Innovations Theory- helps assess and plan for the spread of new ideas, products, or services within a society or other groups. - Characteristics of the innovation - Communication channels - Social system 6. Organizational Change Theory (Series of stages as change is initiated)- Process of change based on various theories that explain change in following: - Organizational structures and processes that influence behavior and motivation for change. - Specific strategies that are required at each stage of change (adopting, implementing, and new approaches)Health promotion theories at their 3 levels*Intrapersonal level* • Stages of Change Theory (Transtheoretical Model) • Health Belief Model (HBM) *Interpersonal level* • Social Learning Theory *Community level* • Community Organization Theory • Diffusion of Innovations Theory • Organizational Change: Stage TheoryDefine Service Learning and experiential, what is the difference?*Experiential learning*- Hands-on learning that is also commonly referred to as practical learning or real-world learning. Experiential learning takes place in authentic settings. Ex: - Community service - Clinical rotation - Practicum/Internship - Volunteerism - Service-learning *Service Learning*- A form of experiential learning in that students engage in cycle of service and reflection. It is a teaching and learning method that stresses collaborative planning among the student, the program faculty, and community partner.How do they both compare to traditional teaching? What are the benefits of each?- Exposure to diverse patient population - Knowledge of community needs & assets - Opportunity to integrate professional training and community service - Communication skills - Personal - Interpersonal skill development - More exposure to the public and IPC.

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