Back

A review of behavioural rating scales for the assessment of Attention Deficit/Hyperactivity Disorder among adults: Efficacy, best practices, and research recommendations.

Grandjean, M. I. V.; Hochman, S.; Mukherjee, R.; Cohen Kadosh, R.

2023-06-03 psychiatry and clinical psychology
10.1101/2023.05.30.23290708 medRxiv
Show abstract

ObjectiveThis review aims to provide researchers with a contemporary and comprehensive understanding of the current state of behavioural rating scales used in evaluating adult ADHD for research purposes. The objective is to offer guidance that enables researchers to make informed decisions when selecting the most suitable scale for their studies. Moreover, our intention was to map and compare these scales, with a specific focus on detecting feigned or invalid symptom presentation--an aspect notably overlooked in prior reviews. MethodWe reviewed the most recent literature on behavioural rating scales for adult ADHD assessment. We evaluated the scales and compared them based on their psychometric properties and the range of symptoms that they assessed. ResultsThe Conners Adult ADHD Rating Scales (CAARS), Mind Excessively Wandering Scale (MEWS), and Wender Utah Rating Scale (WURS) have emerged as the most accurate measures for assessing adult ADHD. Moreover, there is an increasing emphasis on the development of assessment tools, either integrated within existing scales or as independent measures, to evaluate feigning or invalid symptom presentation. In that regard, stand-alone measures have demonstrated greater effectiveness compared to embedded measures, with the ADHD Symptom Infrequency Scale (ASIS) being identified as the most accurate scale for the detection of feigning. ConclusionBased on this review, we provide recommendations for the behavioural rating scales with the most accurate measurement of relevant variables in research-related settings.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.