Subjective executive difficulties – a study using the Dysexecutive Questionnaire
Bibliographic record
Abstract
Introduction: Subjective executive difficulties, understood as a sense of disruption of planning, control and correction of one’s own activity, is often reported by healthy as well as clinical individuals. Self-report measures such as the Dysexecutive Questionnaire (DEX-S) are used to assess the severity of this feeling. The diagnostic value of this method is debated due to the numerous factors affecting the beliefs on executive deficits. Aim of the study: With reference to inconclusive data concerning the underlying factors of subjective executive deficits and the value of self-report measures the following aims of the present study were established: a) determination of the demographic, clinical and cognitive characteristics of individuals with various levels of subjective executive difficulties, b) finding which of these variables contribute to the risk of subjective executive difficulties increase. Material and methods: The study included 213 adult individuals. DEX-S as well as measures of cognitive assessment (Montreal Cognitive Assessment, MoCA; subtests of the Wechsler Adult Intelligence Scale-Revised, WAIS-R) and depressive mood assessment [Geriatric Depression Scale (Short Form), GDS-15] were used. Demographic variables (age, gender and educational level) as well as clinical variables (lack of/presence of central nervous system disease history, including lateralised brain pathology) were also taken into consideration. Based on DEX-S results a cluster analysis was performed and two groups of subjects with a different level of subjective executive difficulties were identified: low-severity group (individuals reporting no complaints regarding executive deficits) and high-severity group (individuals with complaints). Group comparisons demonstrated that individuals complaining about executive deficits have a higher depressive mood index and lower scores on some subtests used to assess cognitive functions. The results of logistic regression analysis suggest that the risk of executive difficulties complaints increases with the severity of depressive mood. In contrast, higher attentional performance reduces the possibility of complaints. No interaction effect was observed between these two factors. Conclusions: Based on the results it can be assumed that there are independent protective mechanisms against subjective executive difficulties as well as mechanisms that exacerbate them, which indicates the need for psychological intervention (e.g. cognitive training and/or psychotherapy) adjusted to the mechanism of the complaint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".