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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
The Uncertainty Principle04:08

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He mathematically...

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General uncertainty.

Alison Moore

    Nursing Standard (Royal College of Nursing (Great Britain) : 1987)
    |July 14, 2012
    PubMed
    Summary
    This summary is machine-generated.

    District general hospitals face service loss and closure due to centralization pressures and funding cuts. Nurse support for reconfiguration hinges on community service improvements to enhance patient care.

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    Area of Science:

    • Healthcare management
    • Health services research
    • Hospital administration

    Background:

    • District general hospitals are under pressure to centralize specialist services.
    • This centralization aims to improve patient outcomes and address funding challenges.
    • Potential service loss and closure of some hospitals are significant concerns.

    Purpose of the Study:

    • To analyze the impact of specialist service centralization on district general hospitals.
    • To explore nurses' willingness to support service reconfiguration.
    • To identify the necessary conditions for successful healthcare service changes.

    Main Methods:

    • Qualitative analysis of hospital service pressures.
    • Assessment of nurse perspectives on service reconfiguration.
    • Review of factors influencing acute and community service integration.

    Main Results:

    • Many district general hospitals are at risk of losing services or closing.
    • Nurses are open to reconfiguration if patient care demonstrably improves.
    • Improvements in community services are crucial for supporting changes in acute services.

    Conclusions:

    • Healthcare reconfiguration requires a balanced approach, considering both specialist centralization and community support.
    • Effective change management must address the needs of both acute and community healthcare settings.
    • Sustaining district general hospitals necessitates strategic planning that integrates service improvements across the healthcare spectrum.