EBP: Class 3 Part 1

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EBP: Class 3 Part 1
2012-08-29 19:05:37
evidence based practice

review of class 3 8/27 for evidence based practice
Show Answers:

  1. What is EBP?
    integration of best research evidence with our clinical expertise and our patient's unique values and circumstances
  2. What are the sources of evidence?
    • patient's unique values and circumstances
    • clinical expertise
    • best research evidence
  3. Name the steps of the scientific method:
    • 1. observe an event
    • 2. develop a hypothesis that makes a prediction
    • 3. test the hypothesis
    • 4. observe the result
    • 5. revise the hypothesis
    • 6. repeat as needed
  4. What are background questions?
    ask for information about a condition
  5. What are the two essential components of a background question?
    • a question root (who, what, where, when, how, why) with a verb
    • a condition or an aspect of a condition
  6. What is a foreground question?
    ask for specific information about managing patients with a condition
  7. What are the 3 essential components of a foreground question?
    • patient and/or problem
    • exposure (intervention) or comparision intervention
    • clinical outcomes
  8. What are the major sources of knowledge/information?
    • tradition
    • authority
    • trial and error
    • logical reasoning
    • the scientific method (research)
  9. List the hierarchy of study types:
    • evidence-based clinical guidelines
    • systematic reviews and meta-analyses of randomized controlled trials
    • randomized controlled trials
    • non-randomized intervention studies
    • observational studies
    • qualitative studies
    • case series, case reports
  10. What is the 3rd step for EBP?
    critically appraise the quality of the research evidence
  11. Critical Appraisal involes understanding:
    • the question(s) being asked
    • the methods used to examin the question(s)
    • -sampling
    • -data collection
    • the statistical analysis of the data
    • -knowing basic terms and how they contribute to interpreting the data
    • the conclusions made by the researchers and deciding if you think they are accurate, appropriate, and/or meaningful to you and the question(s) you have
  12. In research studies what population sample should be used to collect data?
    random sample
  13. What are the scales/levels of data measurement?
    • the mathematical precision with which the values of a variable can be expressed is the levle of measurement
    • nominal-qualitative, categorical
    • ordinal, interval, ratio-quantitative, progressively more precise mathematically
  14. naminal measures:
    • a qualitative (or categorical) level of measurement; has no mathematical interpretation
    • variables whose values vary in kind or quality but not in amount
    • in terms of the variable "occupation", you can say that a lawyer is not equal to a therapist, but you can't say that the lawyer is more occupational or less occupational than the therapist
  15. Ordinal measures:
    • specify only order of cases in "greater than" and "less than" distinctions
    • patient/client satisfaction is an ordinal measure
    • a rehab-specific example is manual muscle testing grades
  16. Interval measures:
    • specific increment but no absolute 0
    • numbers represent fixed measurement units but have no absolute zero point
    • ex: temperature w/ Fahrenheit scale
  17. Ratio Measures:
    • fixed measuring units with an absolute zero point. Zero means absolutely no amount of whatever the varible indicates
    • ratio numbers can be added and subtracted, and because the numbers begin at an absolute zero point, they can be multiplied and divided
    • ex: goniometric measures of ROM
  18. What does reliability of measures mean?
    extent to which a measure produces the same result uner different conditions
  19. Test-retest reliability:
    will the measure produce the same results when given on two different occasions? Typically expressed as a correlation coefficient (r)
  20. Inter-rater reliability:
    the extent to which two or more individuals agree
  21. Intra-rater reliability:
    the degree of agreement among multiple repetitions of a diagnostic test performed by the same individual
  22. Reliability is a property of a:
    measurement instrument...not of an experiment/study.
  23. Validity of Measures:
    extendt to which the measure indicates what it is supposed to measure
  24. face validity:
    is the measure appropriate at face value? Does the measure look like it is going to measure what it is supposed to measure?
  25. Content validity:
    does the measure cover the full range of the concept's meaning?
  26. Criterion validity:
    • can scores obtained with one measure be accurately compared to those obtained using another (more established) measure?
    • two types: concurrent and predictive
  27. Construct validity:
    • a measure should fit well with other measures of similar theoretical concepts
    • Ex: Scores on a marital satisfaction scale should be negatively related to spouse abuse
  28. Internal validity:
    are the methods used in the study correct and are the results accurate?
  29. External validity:
    • Are the findings applicable beyond that particular study?
    • When you have a particular patient/clinical question in mind, you have to ask yourself this question when appraising the literature
  30. KEY POINT: Questions to ask in Critical Apprasial Regarding External Validity:
    • Is the study purpose relevant to your clinical question?
    • Are the study's inclusion and exclusion criteria clearly defined and would the patient in your clinical question qualify for the study?
    • Are the intervention and comparison/control groups receiving an intervention related to your clinical question?
    • Are the outcome measures used in the study relevant to your clinical question and are they conducted in a clinically realistic manner?
    • Is the study population sufficiently similar to the patient in you clinical question to justify expectation that the patient would respond similarly to the population?
  31. Descriptive Statistics:
    • statistical procedures used to summarize, organize, and simplify data
    • patterns can be seen from the organized data
    • they summarize data but don't test for differences or associations
  32. All research reports include descriptive statistics, such as:
    • participant characteristics
    • scores of participants on outcome measure(s)
    • these often are in the participants section of a research report or at the beginning of the results section.
  33. We can describe our data by using a Frequency Distribution. This is usually presented as a table or graph and always presents:
    • the set of sub-categories that made up the original category
    • the frequency of each score/category
  34. What are three important characteristics of frequency distribution?
    • central tendency
    • spread/variability
    • shape
  35. Central tendency:
    • mode
    • median
    • mean
  36. When can mode be used?
    with any type of data
  37. When can median be used?
    interval and ratio data; frequently ordinal data; never nominal data
  38. When can mean be used?
    interval and ratio data; sometimes ordinal data; never nominal data
  39. Spread/variability:
    • Describes in an exact quantitative measure, how spread out/clustered together the measures are
    • Variablility is usually defined in terms of distance
    • range
    • inter-quartile range
    • standard deviation
  40. Range:
    • minimum-maximum
    • simplest and most obvious way of describing spread/variablility
    • Range= highest-lowest
    • the range only takes into account the two extreme scores and ignores any values in between
  41. Inter-quartile range:
    use with medians
  42. Standard deviation is used with?
  43. Mode:
    • most frequent value
    • doesn't take into account exact scores
    • unaffected by extreme scores
    • not useful when there are several values that occur equally often in a set
  44. Median:
    • the values that fall exactly in the midpoint of a ranked distribution
    • does not take into account exact scores
    • unaffected by extreme scores
    • in a small set it can be unrepresentative
  45. Mean (arithmetic average)
    • takes into account all values
    • easily distored by extreme values
    • the mean is the preferred measure of central tendency
  46. When is the mean not the preferred measure of central tendency?
    • when there are extreme scores or skewed distribution
    • non interval data
    • discrete variables
  47. Deviation:
    • a more sophisticated measure of variablility is one that shows how scores cluster around the mean
    • distance of a score from the mean
  48. Standard Deviation:
    a number that measures how far away each number in a set of data is from their mean
  49. If standard deviation is large,
    it means the numbers are spread out from their mean
  50. If standard deviation is small,
    it means the numbers are close to their mean
  51. Frequency Distribution: The normal distribution:
    • bell-shaped
    • symmetrical around the mid point, where the greatest frequency of scores occur
    • in a normal distribution, the mean, median and mode are the same value
  52. KEY POINT: The Beauty of Normal Distribution
    no matter what the mena and standard deviation are for you data set, the area within one standard deviation is about 68% of your data; the area w/in 2 standard deviations is about 95%; and the area w/in 3 standard deviations is aobut 99.7%
  53. population:
    all individuals of interest to the study
  54. sample:
    the particular group of participants you are testing: Selected from population
  55. Statistical inference about populations:
    A treatment that worked for a sample of patients will work for other patients with similar characteristics
  56. Inferential statistics let us estimate population characteristics from:
    sample data
  57. Parameters:
    mathematical characteristics of populations (m)
  58. Statistics:
    mathematical characteristics of samples (x=mean)
  59. Statistics are used to estimate:
  60. Samples must be:
    representative of the population
  61. Representative:
    sample has same characteristics as the population
  62. How can we ensure samples are representative?
    samples drawn according to the rule of Equal Probability of Selection Method: every case in the population has the same chance of being selected for the sample
  63. Central Limit Theorem:
    for any trait or variable, even those that are not normally distributed in the population, as sample size grows larger, the sampling distribution of sample means will become normal in shape
  64. What is a variable?
    • something that varies
    • represent persons or objects that can be manipulated, controlled, or merely measured for the sake of research
    • independent/dependent
  65. Independent variables:
    • ones that are more or less controlled
    • researchers manipulate these variables as they see fit
    • they still vary, but variation is relatively known or taken into account
    • often there are many in a given study
  66. dependent variables:
    • not controlled or manipulated in any way, but are simply measured
    • vary in relation to independent variables, and while results can be predicted, the data is always measured
    • there can be any number of dependent variables, but usually there is one of interest to isolate and study
  67. Independent;
    • intentionally manipulated
    • controlled
    • vary at known rate
    • cause
  68. Dependent:
    • intentionally left alone
    • measured
    • vary at unknown rate
    • effect
  69. Graphing dependent vs. independent variables:
    the dependent variable is placed on the y-axis, while the independent is on the x
  70. What is the goal of research?
    to determine if the independent variable of interest to us has a statistically significant effect on the dependent variable. That means, an effect that is unlikely to be due to chance variations or sampling error
  71. Overview of the Hypothesis Testing Process:
    • 1. State the null hypothesis
    • 2. Look at the data and decide on an appropriate statistical test
    • 3. Compute the statistical test, look at p-value
    • 4. if p-value is less than alpha, reject null hypothesis; if p-value is greater than alpha, fail to reject null hypothesis
  72. null hypothesis:
    • researchers make initial assumption that manipulation of independent variable will have NO EFFECT on the dependent variable (will be null)
    • if any observed difference b/w the experimental and control groups is assumed to be due to chance unless proven otherwise
    • no difference b/w groups, unless by chance
  73. The alternative hypothesis:
    the two means really are differnet, and it's not just chance