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Longitudinal Research02:20

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Health is a condition of the body, mind, and spirit where an individual remains free from illness. Similarly, wellness is an active state, including living a lifestyle that promotes physical, mental, and emotional health. Physical health is critical for the overall well-being and can be affected by lifestyle, activity level, diet, and behavior. The highest attainable standard of health is a fundamental and universal human right. Consider Lisa, a fifteen-year-old born with congenital...
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Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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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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Healthcare Workers Beliefs about COVID-19; a Longitudinal, Mixed Methods Analysis.

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  • 1Department of Clinical Psychology, Mercy University Hospital, Cork, Ireland.

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Area of Science:

  • Psychology
  • Public Health
  • Epidemiology

Background:

  • The psychological impact of COVID-19 on Health Care Workers (HCWs) is significant.
  • Limited research exists on HCWs' personal models of COVID-19 using established theoretical frameworks.
  • Understanding these models is crucial for supporting HCWs during pandemics.

Purpose of the Study:

  • To examine Health Care Workers' beliefs and perceptions regarding COVID-19 during the first and third waves.
  • To investigate the perceived causes of COVID-19 among HCWs.
  • To explore differences in perceptions based on gender and occupational groups.

Main Methods:

  • A mixed-methods study involving 408 HCWs in Ireland during COVID-19 waves.
  • Template analysis of free-text responses from the Brief Illness Perception Questionnaire (B-IPQ).
  • Follow-up analysis on a subset of participants and a new cohort to assess stability and generalizability.

Main Results:

  • Female HCWs reported a greater emotional impact from COVID-19 than males.
  • Occupational differences were found in perceptions of illness timeline, treatment control, and concerns.
  • Administration staff perceived treatments as more helpful and the pandemic as shorter-lasting but expressed higher concern than Health and Social Care Professionals (HSCPs).
  • The primary perceived causes of COVID-19 were 'individual behavioural factors' and 'overseas travel' across both waves.

Conclusions:

  • Health Care Workers' personal models of COVID-19 evolve and are influenced by occupational and individual identities.
  • Template analysis is effective for analyzing qualitative questionnaire data on illness perceptions.
  • Understanding these diverse perceptions is vital for targeted support and interventions for HCWs during health crises.