Publications by authors named "J Christine Dean"

Background: New Zealand guidelines stipulate that patient consent is obtained for medical student involvement in clinical care, however, patients' preferences regarding consent for medical student teaching have not been widely explored. This study examined patient preferences for consent for medical student teaching with the aim to increase patient empowerment, to optimise care and to reflect societal expectations more accurately.

Method: Observational, semi-qualitative, cross-sectional study of in-patients.

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The combination of hypertension with systemic inflammation during pregnancy is a hallmark of preeclampsia, but both processes also convey dynamic information about its antecedents and correlates (e.g., fetal growth restriction) and potentially related offspring sequelae.

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Empathy is characterized as the ability to share one's experience and is associated with altruism. Previous work using blood oxygen level-dependent (BOLD) functional MRI (fMRI) has found that empathy is associated with greater activation in brain mechanisms supporting mentalizing (temporoparietal junction), salience (anterior cingulate cortex; insula), and self-reference (medial prefrontal cortex; precuneus). However, BOLD fMRI has some limitations that may not reliably capture the tonic experience of empathy.

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Background: Young people's sexual health decision-making, including decisions to access and adhere to HIV prevention strategies such as Pre-Exposure Prophylaxis (PrEP), are influenced by a range of internal and external factors. Synthesizing these factors is essential to guide the development of youth-focused PrEP health promotion strategies to contribute to international goals of ending HIV transmission.

Objective: To understand the individual, interpersonal, sociocultural and systemic barriers and facilitators to PrEP access, uptake and use experienced by young people 24 years and younger.

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Balance deficits are present in a variety of clinical populations and can negatively impact quality of life. The integration of wearable sensors and machine learning technology (ML) provides unique opportunities to quantify biomechanical characteristics related to balance outside of a laboratory setting. This article provides a general overview of recent developments in using wearable sensors and ML to estimate or predict biomechanical characteristics such as center of pressure (CoP) and center of mass (CoM) motion.

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