ABSTRACT
The aim was to assess validation and reliability of Arabic version of Children's Hand-use Experience Questionnaire (CHEQ) for children with hemiparetic cerebral palsy (HCP). Ninety-nine children aged 6-18 years diagnosed with HCP participated in the study. The CHEQ was used to evaluate the hand-use experiences of children with cerebral palsy (CP). In the expert opinion questionnaire, the average percentage score of agreement on grasp efficacy was 91.5%. The highest percentage (93.3%) was for time taken. Agreement on CHEQ ranged from 91.5 to 93.3% indicating that the content validity of the prototype is supported by the expert ratings. Fitting indices showed that the one-factor structure of the CHEQ has a good and acceptable fit in children with HCP aged 6-18 years. All factor loads of the CHEQ items were greater than 0.7 and significant. Cronbach's alpha coefficient in this study was 0.921, thus showing that the questionnaire had high internal consistency.
ABSTRACT
The aim of the present study was to examine the psychometric analysis of the Childhood Executive Functioning Inventory (CHEXI) in Saudi Arabian ADHD children using the Rasch model. The study included 210 children from both sexes(males and females). All participants were from Saudi Arabia. Confirmatory factor analysis was performed to determine the dimensional structure of the scale. The Rasch Rating Scale Model (RSM) was used and implemented in the WINSTEPS v. 3.73 program. The results showed that the data, taken together, met the requirements of the RSM fit statistics. A good overall fit of persons and items to the model was found. Persons high rates of endorsement of "definitely true" on the CHEXI, as well as the most difficult items, are at the top of the map. No differences were found between the number of males and females in each of the three areas. The unidimensionality and local independence requirements were met. The levels of difficulty of the response categories are calibrated in ascending order as expected and in agreement with Andreich's scale model, and that all response categories are statistically appropriate according to the two scales of relevance, Infit and Outfit, where the statistics of the mean squares (Mnsq) for the fit of the categories did not exceed the limits of suitability. The thresholds for CHEXI are graded in difficulty and are almost equal in discrimination, and thus the assumption of the rating scale model is fulfilled.
ABSTRACT
Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.
