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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Halo Effect01:27

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Related Experiment Video

Updated: Nov 15, 2025

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

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Is it time to replace the star ratings adherence measures?

Joel F Farley1, Benjamin Y Urick2

  • 1University of Minnesota College of Pharmacy, Minneapolis.

Journal of Managed Care & Specialty Pharmacy
|March 1, 2021
PubMed
Summary
This summary is machine-generated.

Current medication adherence measures in the star ratings program show inflated results and may be easily manipulated. This article suggests improving adherence measurement by expanding medication categories or assessing adherence to all patient medications.

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Area of Science:

  • Health Services Research
  • Pharmacy Quality
  • Health Outcomes

Background:

  • Medication adherence measures for noninsulin diabetes medications (NIDM), statins, and renin angiotensin system antagonists (RASA) have been integral to the star ratings program since 2012.
  • While adherence is crucial, the sustained use of these specific measures presents limitations in accurately reflecting performance.

Purpose of the Study:

  • To critically evaluate the existing star ratings adherence measures.
  • To propose enhancements for more valid and reliable adherence measurement within the program.

Main Methods:

  • Scoping review of current adherence measures.
  • Analysis of star ratings data and payment thresholds over time.
  • Examination of factors potentially influencing adherence reporting.

Main Results:

  • Adherence rates in the star ratings program have significantly increased, with some exceeding clinical literature benchmarks.
  • High adherence rates suggest a ceiling effect, limiting the ability to differentiate plan performance.
  • Practices like mail-order pharmacies and automatic refills may inflate adherence metrics without reflecting true patient behavior.

Conclusions:

  • The current adherence measures may lack validity and are susceptible to manipulation.
  • Consideration should be given to replacing or revising these measures.
  • Potential improvements include broadening the scope of medications or rotating categories annually, or measuring adherence to all patient medications.