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Understanding comorbidity among internalizing problems: Integrating latent structural models of psychopathology and risk mechanisms

Overview of attention for article published in Development & Psychopathology, October 2016
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  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

twitter
2 X users
wikipedia
2 Wikipedia pages

Readers on

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199 Mendeley
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Article details
Title
Understanding comorbidity among internalizing problems: Integrating latent structural models of psychopathology and risk mechanisms
Published in
Development & Psychopathology, October 2016
DOI 10.1017/s0954579416000663
Pubmed ID
Authors
Abstract

It is well known that comorbidity is the rule, not the exception, for categorically defined psychiatric disorders, and this is also the case for internalizing disorders of depression and anxiety. This theoretical review paper addresses the ubiquity of comorbidity among internalizing disorders. Our central thesis is that progress in understanding this co-occurrence can be made by employing latent dimensional structural models that organize psychopathology as well as vulnerabilities and risk mechanisms and by connecting the multiple levels of risk and psychopathology outcomes together. Different vulnerabilities and risk mechanisms are hypothesized to predict different levels of the structural model of psychopathology. We review the present state of knowledge based on concurrent and developmental sequential comorbidity patterns among common discrete psychiatric disorders in youth, and then we advocate for the use of more recent bifactor dimensional models of psychopathology (e.g., p factor; Caspi et al., 2014) that can help to explain the co-occurrence among internalizing symptoms. In support of this relatively novel conceptual perspective, we review six exemplar vulnerabilities and risk mechanisms, including executive function, information processing biases, cognitive vulnerabilities, positive and negative affectivity aspects of temperament, and autonomic dysregulation, along with the developmental occurrence of stressors in different domains, to show how these vulnerabilities can predict the general latent psychopathology factor, a unique latent internalizing dimension, as well as specific symptom syndrome manifestations.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 199 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Netherlands 1 <1%
Unknown 198 99%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 33 17%
Student > Master 24 12%
Researcher 16 8%
Student > Doctoral Student 15 8%
Student > Bachelor 14 7%
Other 26 13%
Unknown 71 36%
Readers by discipline
Readers by discipline Count As %
Psychology 77 39%
Medicine and Dentistry 15 8%
Nursing and Health Professions 6 3%
Neuroscience 4 2%
Social Sciences 4 2%
Other 13 7%
Unknown 80 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 16 January 2026.
All research outputs
#10,711,705
of 34,362,729 outputs
Outputs from Development & Psychopathology
#969
of 2,064 outputs
Outputs of similar age
#112,648
of 336,576 outputs
Outputs of similar age from Development & Psychopathology
#11
of 37 outputs
Altmetric has tracked 34,362,729 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 2,064 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one has gotten more attention than average, scoring higher than 51% 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 336,576 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 37 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 64% of its contemporaries.