Statistically significant discrimination in multiple samples; Areas Under the Curve (AUCs) < .6 under clinically realistic conditions (i.e., not comparing treatment seeking and healthy youth)
AUCs of .60 to <.75 under clinically realistic conditions
AUCs of .75 to .90 under clinically realistic conditions
AUCs >.90 should trigger careful evaluation of research design and comparison group. More likely to be biased than accurate estimate of clinical performance.
*Prescriptive validity
Statistically significant accuracy at identifying a diagnosis with a well-specified matching intervention, or statistically significant moderator of treatment
As “adequate,” with good kappa for diagnosis, or significant treatment moderation in more than one sample
As “good,” with good kappa for diagnosis in more than one sample, or moderate effect size for treatment moderation
Not a problem with the measure or finding, per se; but high predictive validity may obviate need for other assessment components. Compare on utility.
Validity generalization
Some evidence supports use with either more than one specific demographic group or in more than one setting
Bulk of evidence supports use with either more than one specific demographic group or in multiple settings
Bulk of evidence supports use with either more than one specific demographic group and in multiple settings
Not a problem
Treatment sensitivity
Some evidence of sensitivity to change over course of treatment
Independent replications show evidence of sensitivity to change over course of treatment
As good, plus sensitive to change across different types of treatments
Not a problem
Clinical utility
After practical considerations (e.g., costs, ease of administration and scoring, duration, availability of relevant benchmark scores, patient acceptability), assessment data are likely to be clinically useful
As adequate, plus published evidence that using the assessment data confers clinical benefit (e.g., better outcome, lower attrition, greater satisfaction)