Dataset Information

Measuring cognitive errors using the Cognitive Distortions Scale (CDS): psychometric properties in clinical and non-clinical samples.

ABSTRACT: The Cognitive Distortions Scale was developed to assess thinking errors using case examples in two domains: interpersonal and personal achievement. Although its validity and reliability has been previously demonstrated in non-clinical samples, its psychometric properties and scoring has not yet been evaluated. The aim of the current study was to evaluate the psychometric properties of the Cognitive Distortions Scale in two Turkish samples and to examine the usefulness of the categorical scoring system. A total of 325 individuals (Sample 1 and Sample 2) were enrolled in this study to assess those psychometric properties. Our Sample 1 consisted of 225 individuals working as interns at the Diskapi Yildirim Beyazit Teaching and Research Hospital and Sample 2 consisted of 100 patients diagnosed with depression presenting to the outpatient unit of the same Hospital. Construct validity was assessed using the Beck Depression Inventory, the State Trait Anxiety Inventory, the Dysfunctional Attitude Scale, and the Automatic Thought Questionnaire. Factor analyses supported a one-factor model in these clinical and non-clinical samples. Cronbach's α values were excellent in both the non-clinical and clinical samples (0.933 and 0.918 respectively). Cognitive Distortions Scale scores showed significant correlation with relevant clinical measures. Study Cognitive Distortions Scale scores were stable over a time span of two weeks. This study showed that the Cognitive Distortions Scale is a valid and reliable measure in clinical and non-clinical populations. In addition, it shows that the categorical exists/does not exist scoring system is relevant and could be used in clinical settings.

SUBMITTER: Ozdel K

PROVIDER: S-EPMC4149474 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Background Suicide is a public health concern, with an estimated 1 million individuals dying each year worldwide. Individual psychological pain is believed to be a contributing motivating factor. Therefore, establishing a psychometrically sound tool to adequately measure psychological pain is important. The Orbach and Mikulincer Mental Pain Scale (OMMP) has been proposed; however, previous psychometric analysis on the OMMP has not yielded a consistent scale structure, and the internal consistency of the subscales has not met recommended values. Therefore, the primary purpose of this study was to assess the psychometric properties of the OMMP in a diverse sample. Methods A confirmatory factor analysis (CFA) on the 9-factor, 44-item OMMP was conducted on the full sample (n = 1151). Because model fit indices were not met, an exploratory factor analysis (EFA) was conducted on a random subset of the data (n = 576) to identify a more parsimonious structure. The EFA structure was then tested in a covariance model in the remaining subset of participants (n = 575). Multigroup invariance testing was subsequently performed to examine psychometric properties of the refined scale. Results The CFA of the original 9-factor, 44-item OMMP did not meet recommended model fit recommendations. The EFA analysis results revealed a 3-factor, 9-item scale (i.e., OMMP-9). The covariance model of the OMMP-9 indicated further refinement was necessary. Multigroup invariance testing conducted on the final 3-factor, 8-item scale (i.e., OMMP-8) across mental health diagnoses, sex, injury status, age, activity level, and athlete classification met all criteria for invariance. Conclusions The 9-factor, 44-item OMMP does not meet recommended measurement criteria and should not be recommended for use in research and clinical practice in its current form. The refined OMMP-8 may be a more viable option to use; however, more research should be completed prior to adoption. Supplementary Information The online version contains supplementary material available at 10.1186/s42409-021-00025-8.

Project description:BACKGROUND:Care for people with Alzheimer's disease or other dementias should be based on up-to-date clinical practice guidelines. The education and training of nurses and other healthcare staff in dementia is a key factor in providing quality care. Knowledge of Alzheimer's disease can be measured through questionnaires. The aim of this study was to develop and validate a scale to measure Alzheimer's disease knowledge among both nursing staff and students. METHODS:This was a cross-sectional survey study undertaken in three stages: 1) development of the questionnaire and item wording; 2) content validation by an expert panel; 3) questionnaire testing with two samples to establish psychometric properties. Sample 1 comprised 361 Registered Nurses, Assistant Nurses and eldercare workers from 24 nursing homes in Jaén (southern Spain). Sample 2 comprised 297 nursing students. The data were analysed through item analysis and a Rasch model. Convergent and construct validity and internal consistency were also examined. RESULTS:The 23-item UJA Alzheimer's Care Scale shows good outfit and infit values based on the Rasch model. One item presented differential functioning between Registered Nurses and Assistant Nurses. The intraclass correlation coefficient between the UJA Alzheimer's Care Scale and the Spanish version of the Dementia Knowledge Assessment Tool 2 showed strong agreement among nursing staff (0.63) and students (0.79). The scale is able to distinguish between professionals with low or high knowledge of Alzheimer's care. The overall Cronbach's alphas were 0.70 (nursing staff) and 0.82 (nursing students). The intraclass correlation coefficient between the first test and the retest was good (0.84). CONCLUSIONS:The UJA Alzheimer's Care Scale is a useful tool for measuring knowledge of Alzheimer's disease and dementia care among nursing professionals or nursing students. The initial validation study obtained good psychometric properties concerning validity and reliability.

Dataset Information

Measuring cognitive errors using the Cognitive Distortions Scale (CDS): psychometric properties in clinical and non-clinical samples.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets