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Situational factors can influence cognitive performance and should be considered for conducting cognitive assessments. The objective of this project was to develop a checklist for Cognitive Assessment Requirements (CARE) to identify these situational factors before conducting cognitive assessments and account for them. This study employed a four-round Delphi approach involving 22 experts to identify situational factors that can impact cognitive assessment results. The development of a robust and well-balanced checklist was guided by a consensus-driven approach, which considered metrics such as Interquartile Deviation (IQD) (> 1.00), Percentage of Positive Responses (PPR, above 60%), and mean importance ratings (< 3 on a 5-point Likert scale) to assess both degree of agreement and item importance. Consensus was reached, leading to a 14-item checklist to evaluate cognitive assessment requirements. These items were categorized into six groups: Acute Illness or Physical Discomfort, Medication Effects and Substance Use, Sleep Quality and Fatigue, Emotional State, Language factors, and Environmental factors. The CARE can be employed prior to cognitive assessments to identify situational factors of relevance to the individual client, thereby creating a more favorable environment for cognitive evaluation, and enhancing the reliability of the assessment findings. Furthermore, the CARE can help determine the level of confidence in the results by assessing whether the conditions are conducive to testing or if situational factors may undermine the validity of the evaluation. [ABSTRACT FROM AUTHOR]

We examined whether significant scatter in WAIS–IV GAI will reduce its validity to predict performance on WMS–IV indexes. Participants were 330 individuals with neurological, psychiatric, or neurodevelopmental disorders and 59 referrals who were found to be free of a diagnosable disorder. For VCI > PRI, 59.32% were significant at p <.05 and 12.29% were >22 points. For VCI < PRI, 48.37% were significant at p <.05 and 7.19% were >22 points. Inter-subtest scatter across GAI subtests indicated 82.26% of individuals had a significant scatter range and 13.88% had an unusually large range (≥8). For the VCI, 49.10% had significant scatter (≥3) and 12.08% had an unusually large scatter range (≥5). On the PRI, 43.19% had a significant scatter range (≥4) and 12.85% had an unusually large degree of scatter (≥6). Moderation analyses revealed GAI was a significant predictor of each WMS–IV index. The interaction term of GAI with GAI scatter was not significant for any indexes, indicating that regression equations for predicting WMS–IV scores from GAI did not vary significantly across levels of scatter. Estimation of WMS–IV indexes from the GAI is justified even when significant VCI–PRI discrepancies are present and there is unusual variability across the GAI subtests. [ABSTRACT FROM AUTHOR]