OBJECTIVE Suicide ideators and suicide attempters might differ in 3 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 2 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 may be sufficient to distinguish the 2 groups, but no combination may be necessary. This study empirically examined these possibilities. METHOD Across 5 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 variables. Subsequently, the same methods were repeated after removing the top 5 most important or discriminative variables, and a randomly selected 10% subset of variables. Multiple logistic regressions were conducted with all variables. RESULTS Results were consistent across samples. Univariate logistic regressions on average yielded chance-level accuracy. ML algorithms with all variables showed good accuracy; substantial deviation from the optimized algorithms through the removal of variables did not result in significantly poorer performance. Multiple logistic regressions produced poor to fair accuracy. CONCLUSIONS Differences between suicide ideators and attempters are complex. Findings suggest that their differences may be better understood on a psychological primitive level than a biopsychosocial factor level. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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自杀思想家和自杀未遂者之间的区别是：简单，复杂还是复杂？

目的自杀思想家和自杀未遂者可能在3种可能的方式上有所不同。首先，它们可能以简单的方式有所不同，以至于需要一个或一小部分因素来区分两组。其次，思想家和企图者可能会以复杂的方式有所不同，因此需要大量因素的特定组合来进行区分。第三，可能存在复杂的差异：大量因素的许多可能组合可能足以区分这两组，但可能没有必要组合。这项研究从经验上检验了这些可能性。方法对5个样本（总N = 3,869）进行单因素逻辑回归，以检验简单差异。为了测试复杂的差异，机器学习（ML）方法用于识别具有所有变量的优化算法。随后，在删除了前5个最重要或最有区别的变量以及随机选择的10％变量子集之后，重复了相同的方法。对所有变量进行了多元逻辑回归。结果各样本结果一致。平均单变量逻辑回归得出机会级准确性。具有所有变量的ML算法显示出良好的准确性；通过去除变量与优化算法的实质偏差不会导致性能显着下降。多次逻辑回归得出的准确性较差。结论自杀思想家和企图者之间的差异很复杂。研究结果表明，在心理原始水平上比在生物心理社会因素水平上可以更好地理解它们的差异。（PsycINFO数据库记录（c）2020 APA，保留所有权利）。