Publications by authors named "Christy K Y Chan"

Background: The p factor represents the overall liability for the development of mental illness. While evidence supporting the p factor in adults has been reported, studies in children are fewer, and none have examined the p factor in children with chronic physical illness (CPI).

Objective: We aimed to model the p factor in a longitudinal sample of children with CPI using a parent-reported checklist and examine its construct validity against a structured diagnostic interview.

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Data on the chronicity of mental disorder in children with chronic physical illness (CPI) are limited. We examined the prevalence and predictors of homotypic and heterotypic continuity of mental disorder in children with CPI. A sample of 263 children aged 2-16 years with physician-diagnosed CPI were recruited from outpatient clinics (e.

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Objective: This pilot study investigated the feasibility of studying 12-month readmission of youth aged 10-16 years following their first psychiatric hospitalization and changes in youth mental and psychosocial health prospectively.

Results: Inpatient youth with a first psychiatric hospitalization and their parents were recruited from a regional hospital in Canada. Data were collected at recruitment, and at 3-, 6-, and 12-months post-discharge.

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This study uses video and a pretrained deep convolutional neural network to analyze facial photoplethysmographic signals in detection of atrial fibrillation.

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Article Synopsis
  • A study evaluated a new way to screen for atrial fibrillation (AF) using an iPhone camera to detect changes in skin color that signal heartbeats, without needing physical contact.* -
  • Researchers measured photoplethysmographic signals from 217 hospitalized patients, comparing results with traditional ECG readings; the new method showed high sensitivity (95%) and specificity (96%) in identifying AF.* -
  • The findings suggest that using the Cardiio Rhythm app for facial signal detection is a feasible and convenient option for AF screening, offering promising predictive values for accurate results.*
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Background: Upper respiratory tract infection (URTI) has a significant healthcare burden worldwide. Considerable resources are consumed through health care consultations and prescribed treatment, despite evidence for little or no effect on recovery. Patterns of consultations and care including use of symptomatic medications and antibiotics for upper respiratory tract infections are poorly described.

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