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

Design Example: Setting a Curve Using Design Data01:09

Design Example: Setting a Curve Using Design Data

251
Designing and plotting a curve using field data requires precise calculations and execution. A horizontal curve with a radius of 200 meters and an intersection angle of 20 degrees is established using the method of perpendicular offsets from the long chord. The long chord, which spans between the curve's endpoints, is calculated to be 69.46 meters in length. To maintain accuracy in plotting, intervals of 3 meters are selected along the chord.The engineer determines the offset distances for each...
251
Data Reporting and Recording01:24

Data Reporting and Recording

5.5K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.5K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

45.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
45.1K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.4K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.4K
Data Validation01:15

Data Validation

2.1K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
2.1K
Data Validation01:03

Data Validation

6.9K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.9K

You might also read

Related Articles

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

Sort by
Same author

Leveraging Emergency Relief Funding for Behavioral Health Workforce Development: Policy Lessons From Nebraska's Experience.

Psychiatric services (Washington, D.C.)·2026
Same author

Changes to Depression-Related Health Care Encounters and Telehealth Adoption in the COVID-19 Era.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association·2025
Same author

System Effects of Mental Health Agency Expenditures and Mental Health Parity Legislation at the State Level.

The journal of behavioral health services & research·2025
Same author

Looking Toward the Future of Integrated Care: History, Developments, and Opportunities.

The journal of behavioral health services & research·2024
Same author

Implementing automated Medicaid eligibility renewals was not associated with higher levels of program participation.

Health affairs scholar·2024
Same author

Pivot: partisan policy responses to COVID-19 health disparities.

Health affairs scholar·2024

Related Experiment Video

Updated: Feb 10, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.5K

Improving Data for Behavioral Health Workforce Planning: Development of a Minimum Data Set.

Angela J Beck1, Phillip M Singer1, Jessica Buche1

  • 1University of Michigan School of Public Health, Behavioral Health Workforce Research Center, Ann Arbor, Michigan.

American Journal of Preventive Medicine
|May 22, 2018
PubMed
Summary

A behavioral health workforce crisis exists due to shortages and maldistribution. A Minimum Data Set is proposed to standardize data collection and improve planning for essential mental health and substance use services.

More Related Videos

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits
05:08

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits

Published on: March 15, 2024

1.6K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.9K

Related Experiment Videos

Last Updated: Feb 10, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.5K
Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits
05:08

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits

Published on: March 15, 2024

1.6K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.9K

Area of Science:

  • Health Services Research
  • Public Health Policy
  • Workforce Development

Background:

  • The behavioral health field faces a critical workforce shortage impacting service access.
  • Increased demand for services, driven by legislation and healthcare reform, exacerbates this crisis.
  • Current data limitations hinder effective workforce planning and capacity building.

Purpose of the Study:

  • To address limitations in behavioral health workforce data collection.
  • To propose the development of a standardized Minimum Data Set for the behavioral health workforce.
  • To identify challenges in implementing and disseminating this Minimum Data Set.

Main Methods:

  • The article reviews existing data limitations in behavioral health workforce information.
  • It outlines the necessity for standardized data collection approaches.
  • The development and structure of a proposed Minimum Data Set are described.

Main Results:

  • A Minimum Data Set comprising five key data themes was developed.
  • These themes include demographics, licensure, education, occupation, and practice settings.
  • Existing data sources partially align with the Minimum Data Set, but data quality and breadth are deficient.

Conclusions:

  • The Minimum Data Set is a foundational step toward standardizing behavioral health workforce data collection.
  • Standardized data is crucial for effective workforce planning and addressing access to care issues.
  • Overcoming challenges in dissemination and implementation is key for the Minimum Data Set's success.