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Mapping the timescale of suicidal thinking
by
Nock, Matthew K.
, Millner, Alexander J.
, Fortgang, Rebecca G.
, Kleiman, Evan M.
, Ryan, Oisín
, Coppersmith, Daniel D.L.
in
Adult
/ Assessments
/ Autoregressive models
/ Heterogeneity
/ Humans
/ Intention
/ Mathematical models
/ Models, Statistical
/ Monitoring
/ Psychological and Cognitive Sciences
/ Real time
/ Social Sciences
/ Statistical analysis
/ Statistical models
/ Suicidal Ideation
/ Time
/ Time Factors
2023
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Mapping the timescale of suicidal thinking
by
Nock, Matthew K.
, Millner, Alexander J.
, Fortgang, Rebecca G.
, Kleiman, Evan M.
, Ryan, Oisín
, Coppersmith, Daniel D.L.
in
Adult
/ Assessments
/ Autoregressive models
/ Heterogeneity
/ Humans
/ Intention
/ Mathematical models
/ Models, Statistical
/ Monitoring
/ Psychological and Cognitive Sciences
/ Real time
/ Social Sciences
/ Statistical analysis
/ Statistical models
/ Suicidal Ideation
/ Time
/ Time Factors
2023
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Do you wish to request the book?
Mapping the timescale of suicidal thinking
by
Nock, Matthew K.
, Millner, Alexander J.
, Fortgang, Rebecca G.
, Kleiman, Evan M.
, Ryan, Oisín
, Coppersmith, Daniel D.L.
in
Adult
/ Assessments
/ Autoregressive models
/ Heterogeneity
/ Humans
/ Intention
/ Mathematical models
/ Models, Statistical
/ Monitoring
/ Psychological and Cognitive Sciences
/ Real time
/ Social Sciences
/ Statistical analysis
/ Statistical models
/ Suicidal Ideation
/ Time
/ Time Factors
2023
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Journal Article
Mapping the timescale of suicidal thinking
2023
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Overview
This study aims to identify the timescale of suicidal thinking, leveraging real-time monitoring data and a number of different analytic approaches. Participants were 105 adults with past week suicidal thoughts who completed a 42-d real-time monitoring study (total number of observations = 20,255). Participants completed two forms of real-time assessments: traditional real-time assessments (spaced hours apart each day) and high-frequency assessments (spaced 10 min apart over 1 h). We found that suicidal thinking changes rapidly. Both descriptive statistics and Markov-switching models indicated that elevated states of suicidal thinking lasted on average 1 to 3 h. Individuals exhibited heterogeneity in how often and for how long they reported elevated suicidal thinking, and our analyses suggest that different aspects of suicidal thinking operated on different timescales. Continuous-time autoregressive models suggest that current suicidal intent is predictive of future intent levels for 2 to 3 h, while current suicidal desire is predictive of future suicidal desire levels for 20 h. Multiple models found that elevated suicidal intent has on average shorter duration than elevated suicidal desire. Finally, inferences about the within-person dynamics of suicidal thinking on the basis of statistical modeling were shown to depend on the frequency at which data was sampled. For example, traditional real-time assessments estimated the duration of severe suicidal states of suicidal desire as 9.5 h, whereas the high-frequency assessments shifted the estimated duration to 1.4 h.
Publisher
National Academy of Sciences
Subject
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