Clinical versus mechanical prediction: a meta-analysis.

By W. Grove, David H. Zald, Boyd S. Lebow, B. Snitz, Chad R Nelson. 2000

1,676 citations recorded by the source corpus.

Subfields: General Health Professions, Statistics and Probability

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Citations in the corpus

Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2016, background): “…we would like to note that there is a large body of literature showing that data-driven risk assessment instruments tend to be more accurate than professional human judgements [13, 14], and investigating whether human-driven decisions are themselves prone to exhibiting racial bias [15, 16].”
False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks" (2016, neutral): “Noteworthy research on the predictive accuracy of risk assessments can be attributed to Meehl (1954) and Grove et al. (2000), including the oft-cited and comprehensive review of risk assessments from Andrews, Bonta, and Wormith (2006).”
A Commentary on Statistical Assessment of Violence Recidivism Risk (2015, result): “…health and behavior, showing statistical predictions in other fields have commonly matched or outperformed expert clinical judgments (Meehl 1954; Grove et al. 2000; Grove 2005), perhaps because humans are internally wired to impute narrative to data we encounter, and are prone to overdo it…”
The Relationship of Statistical and Psychological Risk Markers to Sexual Reconviction in Child Molesters (2007, neutral): “Static-99 (Hanson & Thornton, 2000) was developed from combining the Structured Anchored Clinical Judgment Scale (SACJ; see Hanson & Thornton, 2000) and the Rapid Risk Assessment for Sexual Offense Recidivism (RRASOR; Hanson, 1997). Static-99 contains 10 items concerned with four broad categories as”
Comparative Validity Analysis of Multiple Measures of Violence Risk in a Sample of Criminal Offenders (2005, background): “This led Grove et al. (2000) to conclude that actuarial prediction is modestly favored Douglas et al. / COMPARATIVE VALIDITY ANALYSIS 503 over clinical prediction, an empirically informed position suggesting that actuarial prediction certainly is not always superior to clinical judgment.”
The Meta-Analysis of Clinical Judgment Project: Fifty-Six Years of Accumulated Research on Clinical Versus Statistical Prediction (2006, methodology): “This confirms, in most instances, the independent and parallel meta-analytic findings of Grove et al. (2000); it is also in accord with earlier narrative reviews of clinical and statistical prediction (e.g., Dawes et al., 1989; Grove & Meehl, 1996; Meehl, 1954; Sawyer, 1966).”
Stubborn Reliance on Intuition and Subjectivity in Employee Selection (2008, background): “…predicting human behavior per se shows that experience does not improve predictions made by clinicians, social workers, parole boards, judges, auditors, admission committees, marketers, and business planners (Camerer & Johnson, 1991; Dawes, Faust, & Meehl, 1989; Grove et al., 2000; Sherden, 1998).”
The accuracy of recidivism risk assessments for sexual offenders: a meta-analysis of 118 prediction studies. (2009, background): “Andrews et al. (2006) classified these measures as first generation, but they are substantially different from the typical forms of unstructured professional judgment studied by Meehl (1954) or Grove et al. (2000).”