Cited by 50Suicide and Self-Harm StudiesMental Health via WritingHomicide, Infanticide, and Child Abuse

The differences between suicide ideators and suicide attempters: simple, complicated, or complex

Huang, Xieyining, Ribeiro, Jessica D., Franklin, Joseph C.·2020·Journal of Consulting and Clinical Psychology

Published version of a preprint

Abstract

Background: Given that many people think about suicide and relatively few attempt suicide, researchers have proposed that accurate differentiation between ideators and attempters can aid suicide prevention efforts. Ideators and attempters might differ in three possible ways. First, they might differ in a simple way such that one or a small set of factors are both necessary and sufficient to distinguish between the two groups. Second, ideators and attempters might differ in a complicated way such that a specific combination of a large set of factors is necessary and sufficient for the distinction. Third, complex differences might exist: many possible combinations of a large set of factors are sufficient to distinguish the two groups, but no combination is necessary. This study empirically examined the difference between suicide ideators and attempters. To support a simple difference, individual factors need to produce high classification accuracy. To support complicated differences, one optimized algorithm including a large number of factors needs to produce high accuracy, but removal of factors from the algorithm should substantially reduce the accuracy. To support complex differences, multiple algorithms including a large number of factors need to produce similarly high accuracy, and meanwhile not all algorithms should lead to high accuracy.Methods: Across five samples (total N = 3,869), univariate logistic regressions were conducted to test for simple differences. To test for complicated and complex differences, Machine learning (ML) methods were used to identify the optimized algorithm with all available variables. Subsequently, the same methods were repeated after removing the top five most important or discriminative variables, and a randomly selected 10% subset of variables. Multiple logistic regressions were conducted with all variables.Results: Results were highly consistent across samples. Univariate logistic regressions on average yielded chance-level accuracy. ML algorithms with all variables or with a subset of variables removed showed good accuracy, whereas multiple logistic regressions produced poor to fair accuracy.Conclusions: The study found that differences between suicide ideators and attempters are complex rather than simple or complicated. The findings are consistent with broader psychological evidence and bear important implications for research and clinical practice.

Integrity signals

Registry

Crossref · 10.1037/ccp0000498

Cites

109 of 109 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Huang, X., Ribeiro, J. D., & Franklin, J. C.
Venue
Journal of Consulting and Clinical Psychology
Published
2020-06
Volume
88
Issue
6
Pages
554-569
ISSN
  • 0022-006X (ISSN-L)
  • 1939-2117
OpenAlex
W3008517898
Cited by
50
References
109
Citations by year
2026: 5 · 2025: 3 · 2024: 9 · 2023: 5 · 2022: 8 · 2021: 11 · 2020: 8 · 2019: 1
FWCI
3.18
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Consulting and Clinical Psychology (publishedVersion)
  • PubMed (publishedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion)
Funders
  • Association for Psychological Science
  • Florida State University
  • Office of the Assistant Secretary of Defense for Health Affairs (W81XWH-10–2-0181)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Social Sciences › Sociology and Political Science › Homicide, Infanticide, and Child Abuse
Keywords
suicidal ideation, suicide attempts, suicide attempters, machine learning, feature importance, biopsychosocial model, suicide risk prediction
MeSH terms
Adolescent, Adult, Female, Humans, Male, Middle Aged, Risk Factors, Suicide, Suicide, Attempted, Young Adult, Suicidal Ideation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review