15 research outputs found

    Abuse, mental state, and health factors pre and during the COVID-19 pandemic: A comparison among clinically referred adolescents in Ontario, Canada

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    Throughout the COVID-19 pandemic, population surveys revealed increased levels of anxiety and depression, while findings from large-scale population data analyses have revealed mixed findings with respect to the mental health consequences for children and youth. The purpose of this study was to examine the impact of the COVID-19 pandemic on the well-being and health-compromising behaviors of adolescents (12–18 years) previously referred for mental health services. Data were collected (pre-pandemic n = 3712; pandemic n = 3197) from mental health agencies across Ontario, Canada using the interRAI Child and Youth Mental Health assessment. Our findings revealed no increased incidence of witnessing domestic violence nor experiencing physical, sexual, or emotional abuse. Further, there were no increases in the risk of self-harm and suicide, anxiety, or depression among our sample of clinically referred youth. Finally, results demonstrated no increase in problematic videogaming/internet use, disordered eating, or alcohol intoxication, and a decrease in cannabis use. Our findings add to the growing body of knowledge as to the impact of the COVID-19 pandemic on children and youth. Further, findings underscore the importance of understanding the nuanced impact of the pandemic on various subgroups of children, youth, and families and highlight the need for continued monitoring of outcomes for these children and youth

    Resource Intensity for Children and Youth: The Development of an Algorithm to Identify High Service Users in Children’s Mental Health

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    Children’s mental health care plays a vital role in many social, health care, and education systems, but there is evidence that appropriate targeting strategies are needed to allocate limited mental health care resources effectively. The aim of this study was to develop and validate a methodology for identifying children who require access to more intense facility-based or community resources. Ontario data based on the interRAI Child and Youth Mental Health instruments were analysed to identify predictors of service complexity in children’s mental health. The Resource Intensity for Children and Youth (RIChY) algorithm was a good predictor of service complexity in the derivation sample. The algorithm was validated with additional data from 61 agencies. The RIChY algorithm provides a psychometrically sound decision-support tool that may be used to inform the choices related to allocation of children’s mental health resources and prioritisation of clients needing community- and facility-based resources

    An evaluation of data quality in Canada’s Continuing Care Reporting System (CCRS): secondary analyses of Ontario data submitted between 1996 and 2011

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    Abstract Background Evidence informed decision making in health policy development and clinical practice depends on the availability of valid and reliable data. The introduction of interRAI assessment systems in many countries has provided valuable new information that can be used to support case mix based payment systems, quality monitoring, outcome measurement and care planning. The Continuing Care Reporting System (CCRS) managed by the Canadian Institute for Health Information has served as a data repository supporting national implementation of the Resident Assessment Instrument (RAI 2.0) in Canada for more than 15 years. The present paper aims to evaluate data quality for the CCRS using an approach that may be generalizable to comparable data holdings internationally. Methods Data from the RAI 2.0 implementation in Complex Continuing Care (CCC) hospitals/units and Long Term Care (LTC) homes in Ontario were analyzed using various statistical techniques that provide evidence for trends in validity, reliability, and population attributes. Time series comparisons included evaluations of scale reliability, patterns of associations between items and scales that provide evidence about convergent validity, and measures of changes in population characteristics over time. Results Data quality with respect to reliability, validity, completeness and freedom from logical coding errors was consistently high for the CCRS in both CCC and LTC settings. The addition of logic checks further improved data quality in both settings. The only notable change of concern was a substantial inflation in the percentage of long term care home residents qualifying for the Special Rehabilitation level of the Resource Utilization Groups (RUG-III) case mix system after the adoption of that system as part of the payment system for LTC. Conclusions The CCRS provides a robust, high quality data source that may be used to inform policy, clinical practice and service delivery in Ontario. Only one area of concern was noted, and the statistical techniques employed here may be readily used to target organizations with data quality problems in that (or any other) area. There was also evidence that data quality was good in both CCC and LTC settings from the outset of implementation, meaning data may be used from the entire time series. The methods employed here may continue to be used to monitor data quality in this province over time and they provide a benchmark for comparisons with other jurisdictions implementing the RAI 2.0 in similar populations.http://deepblue.lib.umich.edu/bitstream/2027.42/112338/1/12911_2012_Article_635.pd

    The Method for Assigning Priority Levels (MAPLe): A new decision-support system for allocating home care resources

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    <p>Abstract</p> <p>Background</p> <p>Home care plays a vital role in many health care systems, but there is evidence that appropriate targeting strategies must be used to allocate limited home care resources effectively. The aim of the present study was to develop and validate a methodology for prioritizing access to community and facility-based services for home care clients.</p> <p>Methods</p> <p>Canadian and international data based on the Resident Assessment Instrument – Home Care (RAI-HC) were analyzed to identify predictors for nursing home placement, caregiver distress and for being rated as requiring alternative placement to improve outlook.</p> <p>Results</p> <p>The Method for Assigning Priority Levels (MAPLe) algorithm was a strong predictor of all three outcomes in the derivation sample. The algorithm was validated with additional data from five other countries, three other provinces, and an Ontario sample obtained after the use of the RAI-HC was mandated.</p> <p>Conclusion</p> <p>The MAPLe algorithm provides a psychometrically sound decision-support tool that may be used to inform choices related to allocation of home care resources and prioritization of clients needing community or facility-based services.</p

    Resource Intensity for Children and Youth: The Development of an Algorithm to Identify High Service Users in Children’s Mental Health

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    Children’s mental health care plays a vital role in many social, health care, and education systems, but there is evidence that appropriate targeting strategies are needed to allocate limited mental health care resources effectively. The aim of this study was to develop and validate a methodology for identifying children who require access to more intense facility-based or community resources. Ontario data based on the interRAI Child and Youth Mental Health instruments were analysed to identify predictors of service complexity in children’s mental health. The Resource Intensity for Children and Youth (RIChY) algorithm was a good predictor of service complexity in the derivation sample. The algorithm was validated with additional data from 61 agencies. The RIChY algorithm provides a psychometrically sound decision-support tool that may be used to inform the choices related to allocation of children’s mental health resources and prioritisation of clients needing community- and facility-based resources

    Validation of the interRAI pressure ulcer risk scale in acute care hospitals

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    OBJECTIVES: To validate the Pressure Ulcer Risk Scale (PURS) to screen for pressure ulcer (PU) outcomes in the acute hospital setting
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