Suspicion of ADHD by teachers in relation to their perception of students’ cognitive capacities: do cognitively strong students escape verdict?

Degroote, Emma; Brault, Marie-Christine; Houtte, Mieke Van

doi:10.1080/13603116.2022.2029962

Cited by 2 publications

(1 citation statement)

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“…When teachers find that students have inattention, hyperactivity and other ADHD-related behavioral problems, they may suggest that the parents take their children to the hospital for evaluation. One study showed that even in a student with strong cognitive ability, teachers were more likely to consider the possibility of ADHD because of inconsistencies between student’s behavioral problems and high cognitive levels ( Degroote et al, 2022 ). In addition, teachers may be more aware of children with combined-type ADHD, but have less awareness about inattentive subtype of ADHD ( Moldavsky et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test

et al. 2023

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BackgroundAttention deficit hyperactivity disorder (ADHD) is a well-studied topic in child and adolescent psychiatry. ADHD diagnosis relies on information from an assessment scale used by teachers and parents and psychological assessment by physicians; however, the assessment results can be inconsistent.PurposeTo construct models that automatically distinguish between children with predominantly inattentive-type ADHD (ADHD-I), with combined-type ADHD (ADHD-C), and without ADHD.MethodsClinical records with age 6–17 years-old, for January 2011–September 2020 were collected from local general hospitals in northern Taiwan; the data were based on the SNAP-IV scale, the second and third editions of Conners’ Continuous Performance Test (CPT), and various intelligence tests. This study used an artificial neural network to construct the models. In addition, k-fold cross-validation was applied to ensure the consistency of the machine learning results.ResultsWe collected 328 records using CPT-3 and 239 records using CPT-2. With regard to distinguishing between ADHD-I and ADHD-C, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 88.75 and 85.56% in the training and testing sets, respectively. The replacement of CPT-2 with CPT-3 results in this model yielded an overall accuracy of 90.46% in the training set and 89.44% in the testing set. With regard to distinguishing between ADHD-I, ADHD-C, and the absence of ADHD, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 86.74 and 77.43% in the training and testing sets, respectively.ConclusionThis proposed model distinguished between the ADHD-I and ADHD-C groups with 85–90% accuracy, and it distinguished between the ADHD-I, ADHD-C, and control groups with 77–86% accuracy. The machine learning model helps clinicians identify patients with ADHD in a timely manner.

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Section: Introductionmentioning

confidence: 99%