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Related Concept Videos

Ranks01:02

Ranks

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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
277
Assessment of the Cardiovascular System I: Subjective Data01:23

Assessment of the Cardiovascular System I: Subjective Data

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A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
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The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like...
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SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
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Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
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Related Experiment Video

Updated: Aug 30, 2025

Oral Health Assessment by Lay Personnel for Older Adults
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Modified Rankin scale assessment by telephone using a simple questionnaire.

Kenichiro Yi1, Makoto Nakajima2, Tomoaki Ikeda1

  • 1Department of Neurology, Minamata City Hospital and Medical Center, Minamata, Japan.

Journal of Stroke and Cerebrovascular Diseases : the Official Journal of National Stroke Association
|September 2, 2022
PubMed
Summary

The Japanese version of the simplified modified Rankin Scale questionnaire (J-RASQ) is a reliable tool for assessing post-stroke disability over the phone. Non-medical staff can easily administer J-RASQ in telephone surveys.

Keywords:
Japanesemodified Rankin Scaleoutcome assessmentquestionnairestroketelephone

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Area of Science:

  • Neurology
  • Clinical Trials
  • Health Services Research

Background:

  • The modified Rankin Scale (mRS) is a standard measure for post-stroke physical disability in clinical trials.
  • Inter-rater variability in mRS assessments can affect trial outcomes.
  • The Japanese version of the simplified mRS questionnaire (J-RASQ) offers a potential solution to reduce discrepancies.

Purpose of the Study:

  • To evaluate the reliability of the J-RASQ when administered via telephone survey.
  • To determine the feasibility of using non-medical staff for J-RASQ administration.
  • To assess J-RASQ's potential to overcome inter-rater discrepancies in mRS scoring.

Main Methods:

  • 103 ischemic stroke patients were recruited for the study.
  • A stroke neurologist performed the standard mRS assessment during an outpatient visit.
  • A clerk administered the J-RASQ via telephone survey on the same day.
  • Agreement between mRS and J-RASQ scores was analyzed using kappa and weighted kappa statistics.

Main Results:

  • The median telephone survey duration was 85 seconds.
  • Overall agreement between mRS and J-RASQ scores was 63.1%.
  • The kappa statistic was 0.51, indicating moderate agreement.
  • The weighted kappa statistic was 0.81, demonstrating substantial agreement, accounting for the degree of disagreement.

Conclusions:

  • The J-RASQ is a reliable tool for assessing post-stroke disability.
  • Telephone surveys administered by non-medical staff using J-RASQ are feasible.
  • J-RASQ can help reduce inter-rater discrepancies in stroke outcome assessments.