Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Expected Value01:15

Expected Value

7.4K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
7.4K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.5K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.5K
Classical Conditioning in Daily Life01:17

Classical Conditioning in Daily Life

2.1K
Classical conditioning, a fundamental principle of associative learning, explains various phenomena observed in daily life, such as fear development, the placebo effect, taste aversion, and drug habituation. These applications demonstrate the profound impact of associative learning on human behavior and physiological responses.
John B. Watson and Rosalie Rayner famously demonstrated the development of fear through classical conditioning in their experiment with Little Albert. They paired the...
2.1K
Variability: Analysis01:11

Variability: Analysis

460
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
460
Random Variables01:09

Random Variables

17.5K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.5K
Variables Affecting Phosphorescence and Fluorescence01:26

Variables Affecting Phosphorescence and Fluorescence

1.3K
Fluorescence and phosphorescence are essential phenomena in fields like analytical chemistry, biological imaging, and materials science, where they detect molecular properties and visualize cellular structures. Understanding the variables that influence these luminescent behaviors is crucial for maximizing accuracy and efficiency in their applications. These variables can broadly be grouped into chemical structure, solvent properties, and external conditions, each playing a distinct role in...
1.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Caring for an Aging America - The Looming Crisis of the Long-Term-Care Workforce.

The New England journal of medicine·2026
Same author

Immigration, the Long-Term Care Workforce, and Elder Outcomes in the US.

American journal of health economics·2026
Same author

Access to Specialty Cancer Care and Plan Disenrollment Among Medicare Beneficiaries.

JAMA network open·2026
Same author

How the Patient Driven Payment Model Shifted Admissions Strategies and Understanding of Care Needs According to Skilled Nursing Facility Administrators.

Medical care research and review : MCRR·2026
Same author

Institutional Special Needs Plans and End-of-Life Outcomes for Nursing Home Residents With Dementia.

JAMA health forum·2026
Same author

Epidemiology of Traumatic Brain Injury in Medicare Beneficiaries over the Last Decade: A Retrospective Observational Study.

Health science reports·2026

Related Experiment Video

Updated: Jan 22, 2026

Author Spotlight: Deciphering the Long-Term Effects of Low-Level Blast Exposures in Mice
06:00

Author Spotlight: Deciphering the Long-Term Effects of Low-Level Blast Exposures in Mice

Published on: May 24, 2024

1.3K

Daily Nursing Home Staffing Levels Highly Variable, Often Below CMS Expectations.

Fangli Geng1, David G Stevenson2, David C Grabowski3

  • 1Fangli Geng ( fgeng@g.harvard.edu ) is a student in the PhD Program in Health Policy, Harvard University Graduate School of Arts and Sciences, in Cambridge, Massachusetts.

Health Affairs (Project Hope)
|July 2, 2019
PubMed
Summary

New payroll data show significant daily and weekend staffing variations in nursing homes. Staffing levels frequently fall below Centers for Medicare and Medicaid Services (CMS) expectations, impacting quality assessments.

Keywords:
Health Care ProvidersNurse staffingNursing Home FacilitiesOrganization and Delivery of CareQuality of careelderly

More Related Videos

Using Learning Outcome Measures to assess Doctoral Nursing Education
10:07

Using Learning Outcome Measures to assess Doctoral Nursing Education

Published on: June 21, 2010

19.4K
Production of Pseudotyped Particles to Study Highly Pathogenic Coronaviruses in a Biosafety Level 2 Setting
08:40

Production of Pseudotyped Particles to Study Highly Pathogenic Coronaviruses in a Biosafety Level 2 Setting

Published on: March 1, 2019

59.7K

Related Experiment Videos

Last Updated: Jan 22, 2026

Author Spotlight: Deciphering the Long-Term Effects of Low-Level Blast Exposures in Mice
06:00

Author Spotlight: Deciphering the Long-Term Effects of Low-Level Blast Exposures in Mice

Published on: May 24, 2024

1.3K
Using Learning Outcome Measures to assess Doctoral Nursing Education
10:07

Using Learning Outcome Measures to assess Doctoral Nursing Education

Published on: June 21, 2010

19.4K
Production of Pseudotyped Particles to Study Highly Pathogenic Coronaviruses in a Biosafety Level 2 Setting
08:40

Production of Pseudotyped Particles to Study Highly Pathogenic Coronaviruses in a Biosafety Level 2 Setting

Published on: March 1, 2019

59.7K

Area of Science:

  • Healthcare quality assessment
  • Nursing home administration
  • Health services research

Background:

  • Staffing levels are a critical quality indicator for nursing homes.
  • The federal Nursing Home Compare website utilizes staffing data.
  • Previous data may not fully capture staffing dynamics.

Purpose of the Study:

  • To analyze new payroll-based data for nursing home staffing.
  • To identify patterns in daily and weekend staffing levels.
  • To compare staffing levels against Centers for Medicare and Medicaid Services (CMS) expectations.

Main Methods:

  • Utilized novel payroll-based data for staffing analysis.
  • Examined daily and weekend staffing fluctuations.
  • Assessed staffing against established benchmarks.

Main Results:

  • Observed substantial daily variations in nursing home staffing.
  • Identified consistently low staffing on weekends.
  • Found that daily staffing often did not meet CMS expectations.

Conclusions:

  • Payroll-based data offer a more precise view of nursing home staffing.
  • Staffing inconsistencies may affect reported quality measures.
  • These findings have implications for CMS and consumers.