Evidence-Based Assessment from Simple Clinical Judgments to Statistical Learning: Evaluating a Range of Options Using Pediatric Bipolar Disorder as a Diagnostic Challenge

Assessment of suicidal thoughts and behaviors in adults: A systematic review of measure psychometric properties and implications for clinical and research utility

High-quality clinical care and research on suicidal thoughts and behaviors (STBs) depends on availability and implementation of reliable and valid measures of STBs. In contrast to studies examining STB risk factors, screening instruments, or treatment, little research has rigorously examined the content, characteristics, and psychometric properties of STB measures themselves. This systematic review (1) identified STB measures that conform to empirically supported definitions of STBs, and (2) identified peer-reviewed papers reporting on the psychometric properties of these measures in adults. Data on psychometric properties and other measure characteristics were extracted. A total of 21 eligible measures were identified in the first stage. In the second stage, 70 articles (with 79 independent samples) were included with psychometric data in adult samples for 19 measures. Although there was support for strong internal consistency and content validity across many measures, face validity and clinical utility concerns were prevalent. Few measures comprehensively assessed suicidal behaviors, and interview-based assessments tended to show the strongest psychometric properties and clinical utility. Findings are discussed in the context of recommendations for improving existing measures, including future research to increase utility and translatability across clinical settings, delivery methods, and diverse populations.

Predictive Utility of Irritability "In Context": Proof-of-Principle for an Early Childhood Mental Health Risk Calculator

OBJECTIVE

We provide proof-of-principle for a mental health risk calculator advancing clinical utility of the irritability construct for identification of young children at high risk for common, early onsetting syndromes.

METHOD

Data were harmonized from two longitudinal early childhood subsamples (total N = 403; 50.1% Male; 66.7% Nonwhite; Mage = 4.3 years). The independent subsamples were clinically enriched via disruptive behavior and violence (Subsample 1) and depression (Subsample 2). In longitudinal models, epidemiologic risk prediction methods for risk calculators were applied to test the utility of the transdiagnostic indicator, early childhood irritability, in the context of other developmental and social-ecological indicators to predict risk of internalizing/externalizing disorders at preadolescence (Mage = 9.9 years). Predictors were retained when they improved model discrimination (area under the receiver operating characteristic curve [AUC] and integrated discrimination index [IDI]) beyond the base demographic model.

RESULTS

Compared to the base model, the addition of early childhood irritability and adverse childhood experiences significantly improved the AUC (0.765) and IDI slope (0.192). Overall, 23% of preschoolers went on to develop a preadolescent internalizing/externalizing disorder. For preschoolers with both elevated irritability and adverse childhood experiences, the likelihood of an internalizing/externalizing disorder was 39-66%.

CONCLUSIONS

Predictive analytic tools enable personalized prediction of psychopathological risk for irritable young children, holding transformative potential for clinical translation.

Development of an optimal short form of the GAD-7 scale with cross-cultural generalizability based on Riskslim

Despite the relatively small number of items in the GAD-7, fewer items are increasingly sought to shorten testing time in large-scale mental health screenings. As a result, short forms based on the GAD-7, the GAD-2, and GAD-mini, have become popular. However, the GAD-2 and GAD-mini have reported lower diagnostic accuracy in some cultural contexts, implying that a validated short-form version of the GAD-7 may be lacking in large-scale cross-cultural anxiety screening. Based on this, to develop an optimal short form of the GAD-7 with cross-cultural stability, we utilized seven GAD-7 datasets from six different countries, totaling 47,484 participants. Five 2 to 6 item short forms of the GAD were constructed using the Riskslim machine learning algorithm. We evaluated the diagnostic accuracy of the GAD-7 short forms in the training and test sets based on the coefficient of determination(R2) and area under the curve(AUC) metrics, and the results showed that GAD-R2 performed poorly in some cultures, and all of the 3 to 6 item short forms of the GAD performed good in cross-cultural diagnostic rates, with the GAD-R6 showing the highest diagnostic accuracy in all cultures; GAD-R3 outperformed GAD-R2, GAD-2, and GAD-mini in all cultures; GAD-R3 had higher generalizability across cultures and special populations; Given that the GAD-R3 was shorter and nearly as accurate as the GAD-R6, we recommend the use of the GAD-R3 in clinical studies and epidemiologic investigations. And we recommend the optimal actual cutoff value of 15 for GAD-R3. Overall, we recommend GAD-R3 as the short-form version of GAD-7 in cross-cultural studies. However, the 2-item GAD scale is also optimal for the short-form version in clinical practice.

Psychometric Properties and Clinical Utility of CBCL and P-GBI Sleep Items in Children and Adolescents

OBJECTIVE

Sleep is crucial to overall health, playing a complex role in a wide range of mental health concerns in children and adults. Nevertheless, clinicians may not routinely assess sleep problems due to lack of awareness or limitations such as cost or time. Scoring sleep-related items embedded on broader scales may help clinicians get more out of tools they are already using. The current study explores evidence of reliability, validity, and clinical utility of sleep-related items embedded on two caregiver-report tools: the Child Behavior Checklist (CBCL) and Parent General Behavior Inventory (P-GBI).

METHOD

Youth aged 5-18 years and their parents were recruited from both an academic medical center (N = 759) and an urban community health center (N = 618). Caregivers completed the CBCL and P-GBI as part of a more comprehensive outpatient evaluation. Exploratory factor analyses, multi-group confirmatory factor analyses, and graded response models evaluated dimensionality, reliability, and invariance across samples. Correlations and receiver operating characteristic curve analyses probed associations with diagnostic and demographic variables.

RESULTS

Two subscales emerged for each itemset. Across both samples, P-GBI sleep subscales were more reliable and consistent than CBCL sleep subscales, showed greater coverage of sleepiness and insomnia constructs, were better at discriminating individuals within a wider range of sleep complaints, and showed significant correlation with mood disorder diagnoses.

CONCLUSIONS

The P-GBI sleep items provide a brief, reliable measure for assessing distinct dimensions of sleep complaints and detecting mood symptoms or diagnoses related to the youth's sleep functioning, making them a useful addition to clinical practice.

Developing Empirical Latent Profiles of Impulsive Aggression and Mood in Youths across Three Outpatient Samples

OBJECTIVE

Aggression with impulsivity and reactivity (AIR) may distinguish a subset of youth from those with attention problems, rule-breaking behavior, or mood disorders, potentially with differential treatment response. Yet, DSM-5 and ICD-10 do not include an AIR diagnosis. Thus, we empirically grouped youths into profiles based on AIR, manic, depressive, rule-breaking, and self-harm behaviors; examined which profiles replicated across three samples; and characterized profile sets on demographic and clinical features.

METHOD

After harmonizing data from three samples (n = 679, n = 392, n = 634), Latent Profile Analysis (LPA) assigned youth to profiles based on caregiver-reported measures of AIR, manic, depressive, rule-breaking, and self-harm behaviors. Profiles from each sample were grouped into sets based on profile similarity. Analyses tested differences in diagnoses, sex, and race, age, functioning, and mood severity.

RESULTS

Eight-profile solutions fit best. Seven profiles replicated across samples: high AIR and self-harm, lower depressive and manic scores; high AIR, manic symptoms, and self-harm; high depression symptoms; three smaller sets with high manic and depressive symptoms and moderate AIR; and two high rates of bipolar diagnoses and family bipolar history. Two sets were high on both AIR and mood symptoms, were the most impaired, and had the highest comorbidity.

CONCLUSIONS

Analyses support an empirical definition of AIR, separate from mood disorders. Profile sets distinguished by level of AIR and mood symptoms differed in demographic and diagnostic characteristics as well as functioning. Importantly, a set emerged with high AIR but low mood indicators and with high rates of ADHD and ODD, but not mood disorder.

Optimized short-forms of the Cognitive Distortions Questionnaire

INTRODUCTION

The Cognitive Distortions Questionnaire (CD-Quest) is a self-report questionnaire that assesses common cognitive distortions. Although the CD-Quest has excellent psychometric properties, its length may limit its use.

METHODS

We attempted to develop short-forms of the CD-Quest using RiskSLIM - a machine learning method to build short-form scales that can be scored by hand. Each short-form was fit to maximize concordance with the total CD-Quest score for a specified number of items based on an objective function, in this case R², by selecting an optimal subset of items and an optimal set of small integer weights. The models were trained in a sample of US undergraduate students (N = 906). We then validated each short-form on five independent samples: two samples of undergraduate students in Brazil (Ns = 182, 183); patients with depression in Brazil (N = 62); patients with social anxiety disorder in the US (N = 198); and psychiatric outpatients in Turkey (N = 269).

RESULTS

A 9-item short-form with integer scoring was created that reproduced the total 15-item CD-Quest score in all validation samples with excellent accuracy (R² = 90.4-93.6%). A 5-item ultra-short-form had good accuracy (R² = 78.2-85.5%).

DISCUSSION

A 9-item short-form and a 5-item ultra-short-form of the CD-Quest both reproduced full CD-Quest scores with excellent to good accuracy. These shorter versions of the full CD-Quest could facilitate measurement of cognitive distortions for users with limited time and resources.

Clinicians' Emotional Reactions toward Patients with Depressive Symptoms in Mood Disorders: A Narrative Scoping Review of Empirical Research

The purpose of this article is to narratively review the empirical literature on clinicians' emotional, cognitive, and behavioral responses (i.e., countertransference) to depressive and other symptoms of patients with mood disorders. Therapist subjective responses (countertransference) can negatively affect both diagnostic and therapeutic processes, especially when they are not recognized and managed promptly. However, at the same time, countertransference recognition, processing, and management can help inform the diagnostic process and improve the therapy process and outcome. In the last couple of decades, the number of studies that empirically explore countertransference toward mood disordered patients, as well as its relationship with various characteristics of both patients and treatment, has increased. Current evidence suggests that patients with depression tend to elicit more positive feelings among clinicians than patients with other severe mental disorders such as borderline personality disorder or schizophrenia. Furthermore, it documents the existence of associations between patients' severity of depressive symptoms and clinicians' subjective reactions, although the results regarding which specific countertransference patterns are evoked in relation to the different phases of the treatment are not entirely consistent. Lastly, growing evidence suggests the presence of clinicians' specific emotional reactions towards patients with suicidal ideation and behavior.

Prevalence of comorbid anxiety and depressive symptomatology in the third trimester of pregnancy: Analysing its association with sociodemographic, obstetric, and mental health features

BACKGROUND

Little is known about the prevalence of comorbid anxiety and depression (CAD) during pregnancy and its risk factors. The aims of this study are to determine the prevalence of CAD in the third trimester of pregnancy and analyse its association with socio-demographic, obstetric, and mental health features.

METHODS

In a sample of 934 Italian pregnant women, CAD was defined as having (1) a score of ≥ 10 on the EPDS - depression subscale and/or on the PHQ-9, and (2) a score of ≥ 40 on the State-Trait Anxiety Inventory State and/or a score of ≥ 6 on the EPDS - anxiety subscale. Logistic regression analyses were used to identify socio-demographic, obstetrics, and mental health risk factors of CAD.

RESULTS

The prevalence of CAD was 6.8%. Age between 30 and 35 years (OR=3.01, 95% CI: 1.22-7.45) compared to younger age, current sleep disorders (OR=7.88, 95% CI: 3.83-16.23), and preconception mood disorders (OR=2.76, 95% CI: 1.31-5.84) were associated with higher odds of CAD. Conversely, the presence of no or few economic problems (OR=0.21, 95% CI: 0.07-0.65; OR=0.26, 95% CI: 0.09-0.77) and the perception of enough or more than enough practical support from friends or relatives (OR=0.32, 95% CI: 0.13-0.80; OR=0.22, 95% CI: 0.09-0.53) were associated with lower odds of developing CAD.

LIMITATIONS

The cross-sectional design; the use of self-report questionnaires.

CONCLUSION

CAD is relatively common among third-trimester antepartum women. The provision of economic/practical support may reduce CAD prevalence and its direct and indirect costs.

Leveraging Machine Learning to Identify Predictors of Receiving Psychosocial Treatment for Attention Deficit/Hyperactivity Disorder

This study aimed to identify factors associated with receiving psychosocial treatment for ADHD in a nationally representative sample. Participants were 6630 youth with a parent-reported diagnosis of ADHD from the 2016-2017 National Survey of Children's Health. Machine learning analyses were performed to identify factors associated with receipt of psychosocial treatment for ADHD. We examined potentially associated factors in the broad categories of variables hypothesized to affect problem recognition (e.g., severity, mental health comorbidities); the decision to seek treatment; service selection (e.g., insurance coverage) and service use. We found that three machine learning models unanimously identified parent-reported ADHD severity (mild vs. moderate/severe) as the factor that best distinguishes between children who receive psychosocial treatment for ADHD and those who do not. Receive operating characteristic curve analysis revealed the following model performance: classification and regression tree analysis (area under the curve; AUC = .68); an ensemble model (AUC = .71); and a deep, multi-layer neural network (AUC = .72), as well as comparison to a logistic regression model (AUC = .69). Further, insurance coverage of mental/behavioral health needs emerged as a salient factor associated with the receipt of psychosocial treatment. Machine learning models identified risk and protective factors that predicted the receipt of psychosocial treatment for ADHD, such as ADHD severity and health insurance coverage.

Generalizing the Prediction of Bipolar Disorder Onset Across High-Risk Populations

OBJECTIVE

Risk calculators (RC) to predict clinical outcomes are gaining interest. An RC to estimate risk of bipolar spectrum disorders (BPSD) could help reduce the duration of undiagnosed BPSD and improve outcomes. Our objective was to adapt an RC previously validated in the Pittsburgh Bipolar Offspring Study (BIOS) sample to achieve adequate predictive ability in both familial high-risk and clinical high-risk youths.

METHOD

Participants (aged 6-12 years at baseline) from the Longitudinal Assessment of Manic Symptoms (LAMS) study (N = 473) were evaluated semi-annually. Evaluations included a Kiddie Schedule for Affective Disorders (K-SADS) interview. After testing an RC that closely approximated the original, we made modifications to improve model prediction. Models were trained in the BIOS data, which included biennial K-SADS assessments, and tested in LAMS. The final model was then trained in LAMS participants, including family history of BPSD as a predictor, and tested in the familial high-risk sample.

RESULTS

Over follow-up, 65 youths newly met criteria for BPSD. The original RC identified youths who developed BPSD only moderately well (area under the curve [AUC] = 0.67). Eliminating predictors other than the K-SADS screening items for mania and depression improved accuracy (AUC = 0.73) and generalizability. The model trained in LAMS, including family history as a predictor, performed well in the BIOS sample (AUC = 0.74).

CONCLUSION

The clinical circumstances under which the assessment of symptoms occurs affects RC accuracy; focusing on symptoms related to the onset of BPSD improved generalizability. Validation of the RC under clinically realistic circumstances will be an important next step.

The Collateral Impact of COVID-19 Emergency on Neonatal Intensive Care Units and Family-Centered Care: Challenges and Opportunities

The ongoing Coronavirus disease 2019 (COVID-19) pandemic is disrupting most specialized healthcare services worldwide, including those for high-risk newborns and their families. Due to the risk of contagion, critically ill infants, relatives and professionals attending neonatal intensive care units (NICUs) are undergoing a profound remodeling of the organization and quality of care. In particular, mitigation strategies adopted to combat the COVID-19 pandemic may hinder the implementation of family-centered care within the NICU. This may put newborns at risk for several adverse effects, e.g., less weight gain, more nosocomial infections, increased length of NICU stay as well as long-term worse cognitive, emotional, and social development. This article aims to contribute to deepening the knowledge on the psychological impact of COVID-19 on parents and NICU staff members based on empirical data from the literature. We also provided evidence-based indications on how to safely empower families and support NICU staff facing such a threatening emergency, while preserving the crucial role of family-centered developmental care practices.

Predictors of Hospitalization in a Cohort of Children with Elevated Symptoms of Mania

Describe hospitalization rates in children with elevated symptoms of mania and determine predictors of psychiatric hospitalizations during the 96 month follow-up. Eligible 6-12.9 year olds and their parents visiting 9 outpatient mental health clinics were invited to be screened with the Parent General Behavior Inventory 10-item Mania Scale. Of 605 children with elevated symptoms of mania eligible for follow-up, 538 (88.9%) had ≥ 1 of 16 possible follow-up interviews and are examined herein. Multivariate Cox regression indicated only four factors predicted hospitalizations: parental mental health problems (HR 1.80; 95% CI 1.21, 2.69); hospitalization prior to study entry (HR 3.03; 95% CI 1.80, 4.43); continuous outpatient mental health service use (HR 3.73; 95% CI 2.40, 5.50); and low parental assessment of how well treatment matched child's needs (HR 3.97; 95% CI 2.50, 6.31). Parental perspectives on mental health services should be gathered routinely, as they can signal treatment failures.

Evaluating and Validating GBI Mania and Depression Short Forms for Self-Report of Mood Symptoms

Objective: To evaluate short forms of free self-report mania and depression scales, evaluating their reliability, content coverage, criterion validity, and diagnostic accuracy.Method: Youths age 11 to 18 years seeking outpatient mental health services at either an Academic medical clinic (N = 427) or urban Community mental health center (N = 313), completed the General Behavior Inventory (GBI) and other rating scales. Youths and caregivers completed semi-structured interviews to establish diagnoses and mood symptom severity, with GBI scores masked during diagnosis. Ten- and seven-item short forms, psychometric projections, and observed performance were tested first in the Academic sample and then externally cross-validated in the Community sample.Results: All short forms maintained high reliability (all alphas >.80 across both samples), high correlations with the full-length scales (r.85 to.96), excellent convergent and discriminant validity with mood, behavior, and demographic criteria, and diagnostic accuracy undiminished compared to using the full-length scales. Ten-item scales showed advantages in terms of coverage; the 7 Up showed slightly weaker performance.Conclusions: Present analyses evaluated and externally cross-validated short forms that maintain high reliability and content coverage, and show strong criterion validity and diagnostic accuracy - even when used in an independent sample with very different demographics and referral patterns. The short forms appear useful in clinical applications including initial evaluation, as well as in research settings where they offer an inexpensive quantitative score. Short forms are available in more than two dozen languages. Future work should further evaluate sensitivity to treatment effects and cultural invariance.

Real-Time Monitoring of Suicide Risk among Adolescents: Potential Barriers, Possible Solutions, and Future Directions

Recent advances in real-time monitoring technology make this an exciting time to study risk for suicidal thoughts and behaviors among youth. Although there is good reason to be excited about these methods, there is also reason for caution in adopting them without first understanding their limitations. In this article, we present several broad future directions for using real-time monitoring among youth at risk for suicide focused around three broad themes: novel research questions, novel analytic methods, and novel methodological approaches. We also highlight potential technical, logistical, and ethical challenges with these methodologies, as well as possible solutions to these challenges.

Is the Finding Too Good to Be True? Moving from "More Is Better" to Thinking in Terms of Simple Predictions and Credibility

In 2018, De Los Reyes and Langer expanded the scope of the Evidence Base Updates series to include reviews of psychological assessment techniques. In keeping with the goal of offering clear "take-home messages" about the evidence underlying the technique, experts have proposed a rubric for evaluating the reliability and validity support. Changes in the research environment and pressures in the peer review process, as well as a lack of familiarity with some statistical methods, have created a situation in which many findings that appear "excellent" in the rubric are likely to be "too good to be true," in the sense that they are unlikely to generalize to clinical settings or are unlikely to be reproduced in independent samples. We describe several common scenarios in which published results are often too good to be true, including internal consistency, interrater reliability, correlation, standardized mean differences, diagnostic accuracy, and global model fit statistics. Simple practices could go a long way toward improving design, reporting, and interpretation of findings. When effect sizes are in the "excellent" range for issues that have been challenging, scrutinize before celebrating. When benchmarks are available based on theory or meta-analyses, results that are moderately better than expected in the favorable direction (i.e., Cohen's q ≥ +.30) also invite critical appraisal and replication before application. If readers and reviewers pull for transparency and do not unduly penalize authors who provide it, then change in research quality will be faster and both generalizability and reproducibility are likely to benefit.

Predicting Adolescent Substance Use in a Child Welfare Sample: A Multi-Indicator Algorithm

Given the risk of substance use (SU) among adolescents in the child welfare system, identification of risk for prospective impairing SU behaviors is a significant public health priority. We sought to quantify the incremental validity of routine multi-informant assessments of adolescent psychological distress (i.e., the Child Behavior Checklist and Youth Self-Report) and a commonly used SU screening protocol (i.e., the CRAFFT) to predict SU at 18 and 36 months after baseline in a nationally representative child welfare sample (N = 1,054; M_age = 13.72). We used receiver operator characteristics and reclassification analyses to develop our algorithms. We found that a battery consisting of baseline CRAFFT scores, self-reported delinquent behavior, and parent-reported rule-breaking behavior provided an incrementally valid prediction model for SU behavior among females, while baseline CRAFFT scores and self-reported delinquent behavior incrementally predicted SU for males. Results suggest that leveraging existing assessments within the child welfare system can improve forecasting of SU risk for this population.

Developing and Validating a Definition of Impulsive/Reactive Aggression in Youth

The goal of this study is to develop a rational data-driven definition of impulsive/reactive aggression and establish distinctions between impulsive/reactive aggression and other common childhood problems. This is a secondary analysis of data from Assessing Bipolar: A Community Academic Blend (ABACAB; N = 636, ages 5-18), Stanley Medical Research Institute N = 392, ages 5-17), and the Longitudinal Assessment of Manic Symptoms (LAMS; N = 679, ages 6-12) studies, which recruited youths seeking outpatient mental health services in academic medical centers and community clinics. Following Jensen et al.'s (2007) procedure, 3 judges independently rated items from several widely used scales in terms of assessing impulsive/reactive aggression. Principal components analyses (PCA) modeled structure of the selected items supplemented by items related to mood symptoms, rule-breaking behavior, and hyperactivity/impulsivity to better define the boundaries between impulsive/reactive aggression and other common childhood symptoms. In the rational item selection process, there was good agreement among the 3 experts who rated items as characterizing impulsive/reactive aggression or not. PCA favored 5 dimension solutions in all 3 samples. Across all samples, PCA resulted in rule-breaking behavior, aggression-impulsive/reactive (AIR), mania, and depression dimensions; there was an additional hyperactive/impulsive dimension in the LAMS sample and a self-harm dimension in ABACAB and Stanley samples. The dimensions demonstrated good internal consistency; criterion validity coefficients also showed consistency across samples. This study is a step toward developing an empirically derived nosology of impulsive aggression/AIR. Findings support the validity of the AIR construct, which can be distinguished from manic and depressive symptoms as well as rule-breaking behavior.

Informant discrepancies in assessments of psychosocial functioning in school-based services and research: Review and directions for future research

Psychosocial functioning plays a key role in students' wellbeing and performance inside and outside of school. As such, techniques designed to measure and improve psychosocial functioning factor prominently in school-based service delivery and research. Given that the different contexts (e.g., school, home, community) in which students exist vary in the degree to which they influence psychosocial functioning, educators and researchers often rely on multiple informants to characterize intervention targets, monitor intervention progress, and inform the selection of evidence-based services. These informants include teachers, students, and parents. Across research teams, domains, and measurement methodologies, researchers commonly observe discrepancies among informants' reports. We review theory and research-occurring largely outside of school-based service delivery and research-that demonstrates how patterns of informant discrepancies represent meaningful differences that can inform our understanding of psychosocial functioning. In turn, we advance a research agenda to improve use and interpretation of informant discrepancies in school-based services and research.

A multimethod screening approach for pediatric depression onset: An incremental validity study

OBJECTIVE

Screening protocols that rely on a single informant are inadequate in predicting pediatric depression. Multi-informant and risk factor screening approaches are potentially more sensitive methods for identifying depression risk, but the incremental validity of these protocols has not been adequately tested. Using a translational analytic approach and multimethod, longitudinal study design, we simultaneously tested several multi-indicator approaches to depression screening to identify an optimal algorithm for predicting depression onset in youth.

METHOD

Participants were 222 never-depressed children and adolescents (M_age = 10.75 years old, SD_age = 1.85; female = 50.45%; 82.88% White), who completed baseline questionnaires for depressive symptoms and cognitive vulnerabilities, in addition to a morphed face task to assess pupil dilation. Mothers, meanwhile, completed baseline questionnaires and a semistructured interview to assess maternal and pediatric depression. Follow-up depression diagnostic assessments with both the mother and youth occurred every 6 months for 2 years. Receiver operating characteristics and reclassification analyses were used to test our aims.

RESULTS

Overall, we found moderate support for a multi-informant approach, and convincing evidence that individual differences in pupil dilation uniquely predicted depression onset. Youth with subthreshold depressive symptoms and elevated pupil dilation were over twice as likely to develop a first lifetime episode of depression compared to one's risk rate based on sex and age.

CONCLUSIONS

Our study provides one of the first screening batteries for detecting first lifetime episodes of depression in youth. The unique and incremental validity provided by pupil dilation suggests feasible biological indicators of depression risk can improve primary prevention efforts that target depression, such as universal pediatric depression screening. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The Importance of Calibration in Clinical Psychology

Accuracy has several elements, not all of which have received equal attention in the field of clinical psychology. Calibration, the degree to which a probabilistic estimate of an event reflects the true underlying probability of the event, has largely been neglected in the field of clinical psychology in favor of other components of accuracy such as discrimination (e.g., sensitivity, specificity, area under the receiver operating characteristic curve). Although it is frequently overlooked, calibration is a critical component of accuracy with particular relevance for prognostic models and risk-assessment tools. With advances in personalized medicine and the increasing use of probabilistic (0% to 100%) estimates and predictions in mental health research, the need for careful attention to calibration has become increasingly important.