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Uncertainty: Overview00:59

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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.
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Propagation of Uncertainty from Random Error00:59

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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...
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Propagation of Uncertainty from Systematic Error01:10

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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...
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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...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Principle of Moments: Problem Solving01:30

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The principle of moments is a fundamental concept in physics and engineering. It refers to the balancing of forces and moments around a point or axis, also known as the pivot. This principle is used in many real-life scenarios, including construction, sports, and daily activities like opening doors and pushing objects.
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Into the Unknown: Characterizing Fellow Uncertainty During the Transition to Unsupervised Practice.

Daniel Herchline1, Margot E Cohen2, Marietta Ambrose3

  • 1is an Assistant Professor, Department of Pediatrics, Cincinnati Children's Hospital Medical Center.

Journal of Graduate Medical Education
|May 4, 2023
PubMed
Summary
This summary is machine-generated.

Physician fellows face individualized uncertainty during their transition to unsupervised practice, stemming from clinical competence, employment, and career vision. They utilize strategies like graduated autonomy and professional networks to manage these challenges.

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Area of Science:

  • Medical Education
  • Professional Development
  • Physician Training

Background:

  • Medical education increasingly focuses on managing patient-related uncertainty.
  • Less attention is given to how fellows manage professional transition uncertainty.
  • Understanding fellows' transition experiences can aid fellows, programs, and institutions.

Purpose of the Study:

  • To explore how fellows in the United States experience uncertainty during their transition to unsupervised practice.

Main Methods:

  • Constructivist grounded theory approach.
  • Semi-structured interviews with 18 physicians in their final year of fellowship.
  • Interviews conducted between September 2020 and March 2021 at two large academic institutions.
  • Inductive coding for data analysis.

Main Results:

  • Uncertainty experiences during transition are individualized and dynamic.
  • Key sources of uncertainty include clinical competence, employment prospects, and career vision.
  • Mitigation strategies involve graduated autonomy, professional networking, and institutional support.

Conclusions:

  • Fellows' transition to unsupervised practice involves individualized, contextual, and dynamic uncertainty.
  • Shared themes emerge regarding the nature and management of this uncertainty.