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Related Concept Videos

Data Collection II01:29

Data Collection II

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Data Collection by Experiments01:13

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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Data Collection I01:30

Data Collection I

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Data Collection III01:05

Data Collection III

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Quantifying Work02:30

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As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system.
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A "datathon" model to support cross-disciplinary collaboration.

Jerôme Aboab1, Leo Anthony Celi1, Peter Charlton1

  • 1Massachusetts Institute of Technology (MIT) Critical Data, MIT, Cambridge, MA 02139, USA.

Science Translational Medicine
|April 8, 2016
PubMed
Summary
This summary is machine-generated.

Datathons, or hackathons, can improve the reliability of biomedical research by bringing diverse experts together. Continuous peer review through follow-up datathons may enhance study design and analysis for more dependable scientific findings.

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Area of Science:

  • Biomedical research
  • Clinical research
  • Health care innovation

Background:

  • Growing concerns regarding the reliability of published biomedical and clinical research.
  • Need for novel approaches to integrate new scientific contributors into health care.

Purpose of the Study:

  • To propose a datathon model for enhancing the reliability of scientific research.
  • To foster collaboration among individuals with diverse expertise to address clinical questions.

Main Methods:

  • Implementing a datathon (or hackathon) model.
  • Bringing together participants with complementary knowledge and skills.
  • Utilizing continuous peer review through follow-up datathons for ongoing projects.

Main Results:

  • The proposed model facilitates the synergistic combination of disparate expertise.
  • Continuous peer review offers new perspectives on study design and methodology.
  • Follow-up datathons can lead to replication of prior analyses, enhancing reliability.

Conclusions:

  • Datathons offer a promising framework for producing more reliable biomedical and clinical research.
  • This model can effectively introduce and integrate new scientific contributors into the health care culture.
  • Continuous peer review within the datathon structure is key to improving research dependability.