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Bias in Epidemiological Studies01:29

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Implicit and Explicit Weight Bias among Midwives: Variations Across Demographic Characteristics.

Heather M Bradford1,2, Rebecca M Puhl3, Julia C Phillippi2

  • 1Georgetown University School of Nursing, Washington, District of Columbia.

Journal of Midwifery & Women'S Health
|March 15, 2024
PubMed
Summary

Weight bias in midwives is linked to lower body mass index (BMI) and older age. Black midwives showed less explicit bias than White midwives, highlighting demographic variations in healthcare provider bias.

Keywords:
body mass indexhealth caremidwiferyobesitypregnancyweight biasweight prejudiceweight stigmaweight‐based discrimination

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Area of Science:

  • Healthcare Disparities
  • Medical Sociology
  • Public Health

Background:

  • Weight bias is prevalent in healthcare settings.
  • Limited quantitative data exists on weight bias among perinatal care providers.
  • Demographic variations in weight bias among midwives are not well understood.

Purpose of the Study:

  • To examine weight bias among American Midwifery Certification Board (AMCB) certified midwives.
  • To investigate variations in weight bias based on age, years since certification, BMI, race, ethnicity, and geographic region.
  • To quantify implicit and explicit weight bias in a national sample of midwives.

Main Methods:

  • Online survey distributed via email listservs, social media, and professional networks.
  • Assessment of implicit weight bias using the Implicit Association Test.
  • Measurement of explicit weight bias using the Anti-Fat Attitudes Questionnaire (AFA), Fat Phobia Scale (FPS), and Preference for Thin People (PTP) measure.

Main Results:

  • 2106 midwives participated, identifying as Black or White across 4 US regions.
  • Lower BMI and younger age correlated with higher implicit and explicit weight bias.
  • Age and BMI were significant predictors after adjustments; Black midwives showed lower explicit bias on some measures.
  • Implicit bias varied by age and years since certification, with lower levels in younger/less experienced midwives.

Conclusions:

  • This is the first quantitative study on demographic variations in midwife weight bias.
  • Further research with diverse samples is necessary.
  • Investigating the impact of weight bias on clinical decision-making and care quality is warranted.