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Youth at-risk for serious mental illness: methods of the PROCAN study

Overview of attention for article published in BMC Psychiatry, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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17 X users
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Title
Youth at-risk for serious mental illness: methods of the PROCAN study
Published in
BMC Psychiatry, July 2018
DOI 10.1186/s12888-018-1801-0
Pubmed ID
Authors

Jean Addington, Benjamin I. Goldstein, Jian Li Wang, Sidney H. Kennedy, Signe Bray, Catherine Lebel, Stefanie Hassel, Catherine Marshall, Glenda MacQueen

Abstract

Most mental disorders begin in adolescence; however, there are gaps in our understanding of youth mental health. Clinical and policy gaps arise from our current inability to predict, from amongst all youth who experience mild behavioural disturbances, who will go on to develop a mental illness, what that illness will be, and what can be done to change its course and prevent its worsening to a serious mental illness (SMI). There are also gaps in our understanding of how known risk factors set off neurobiological changes that may play a role in determining who will develop a SMI. Project goals are (i) to identify youth at different stages of risk of SMI so that intervention can begin as soon as possible and (ii) to understand the triggers of these mental illnesses. This 2-site longitudinal study will recruit 240 youth, ages 12-25, who are at different stages of risk for developing a SMI. The sample includes (a) healthy individuals, (b) symptom-free individuals who have a first-degree relative with a SMI, (c) youth who are experiencing distress and may have mild symptoms of anxiety or depression, and (d) youth who are already demonstrating attenuated symptoms of SMI such as bipolar disorder or psychosis. We will assess, every 6 months for one year, a wide range of clinical and psychosocial factors to determine which factors can be used to predict key outcomes. We will also assess neuroimaging and peripheral markers. We will develop and validate a prediction algorithm that includes demographic, clinical and psychosocial predictors. We will also determine if adding biological markers to our algorithm improves prediction. Outcomes from this study include an improved clinical staging model for SMI and prediction algorithms that can be used by health care providers as decision-support tools in their practices. Secondly, we may have a greater understanding of clinical, social and cognitive factors associated with the clinical stages of development of a SMI, as well as new insights from neuroimaging and later neurochemical biomarker studies regarding predisposition to SMI development and progression through the clinical stages of illness.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 201 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 23 11%
Student > Bachelor 20 10%
Researcher 19 9%
Student > Ph. D. Student 18 9%
Student > Doctoral Student 11 5%
Other 27 13%
Unknown 83 41%
Readers by discipline Count As %
Psychology 49 24%
Medicine and Dentistry 17 8%
Neuroscience 11 5%
Nursing and Health Professions 9 4%
Social Sciences 6 3%
Other 19 9%
Unknown 90 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 October 2018.
All research outputs
#2,767,764
of 22,925,760 outputs
Outputs from BMC Psychiatry
#999
of 4,717 outputs
Outputs of similar age
#58,233
of 326,886 outputs
Outputs of similar age from BMC Psychiatry
#40
of 120 outputs
Altmetric has tracked 22,925,760 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,717 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.9. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 326,886 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.