Related Experiment Video
Updated: Apr 11, 2026
04:58
Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
Published on: December 13, 2024
4.8K
Normalizing Rejection.
Vicki S Conn1, Julie Zerwic2, Urmeka Jefferson3
1University of Missouri, Columbia, USA conn@missouri.edu.
Western Journal of Nursing Research
|June 5, 2015
Summary
Grant and manuscript rejection are common for nurse researchers. Normalizing rejection helps overcome negative emotions and use feedback to advance scientific programs.
Area of Science:
- Nursing Research
- Scientific Communication
Background:
- Grant funding and manuscript submissions are critical for nurse researchers.
- Rejection from these processes can negatively impact emotional well-being and productivity.
- Acknowledge rejection as a normal part of scientific advancement.
Purpose of the Study:
- To provide practical strategies for nurse researchers to cope with rejection.
- To guide researchers in constructively addressing reviewer comments.
- To help researchers reframe rejection as an opportunity for growth.
Main Methods:
- The article discusses the psychological impact of rejection.
- It offers advice on emotional processing and acceptance.
- Methods for analyzing and responding to reviewer feedback are presented.
Main Results:
- Normalizing rejection facilitates quicker emotional recovery.
- Constructive engagement with feedback can refine research projects.
- Overcoming negative emotions associated with rejection enhances productivity.
Conclusions:
- Accepting rejection as integral to the scientific process is key.
- Utilizing reviewer comments effectively advances research programs.
- Strategies for emotional resilience and constructive feedback management are essential for nurse researchers.
Related Concept Videos
01:12Sampling Distribution
19.8K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
19.8K
01:09Random Sampling Method
15.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.9K
01:02Quantifying and Rejecting Outliers: The Grubbs Test
5.0K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
5.0K
01:16Contaminants and Errors
619
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...
619
01:16The Anderson-Darling Test
1.2K
The Anderson-Darling test is a statistical method used to determine whether a data sample is likely drawn from a specific theoretical distribution. Unlike parametric tests, it does not require assumptions about specific parameters of the distribution. Instead, it compares the sample's empirical cumulative distribution function (ECDF) with the cumulative distribution function (CDF) of the hypothesized distribution. Critical values for the test are specific to the chosen distribution rather...
1.2K
01:00Detection of Gross Error: The Q Test
8.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
8.1K