ADHD and anxiety symptom comorbidity from an event segmentation lens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ADHD and anxiety present high comorbidity, including symptom overlap and related diagnostic and treatment challenges. The current study aimed to extend this area of research by investigating the event segmentation patterns of those with ADHD and anxiety symptoms. Event segmentation is the process of parsing a continuous flow of information into meaningful events, providing the opportunity to examine similarities and differences in how these groups organize their perception of daily experiences. Participants performed an event segmentation task consisting of watching four short movies and identifying large and small events. We used the total number of button presses and segmentation agreement scores in a multivariate analysis, and results indicated that the High ADHD group identified significantly more events in the large condition than the High Anxiety group and had significantly higher agreement scores than the Comorbid group. This study furthers our understanding of the cognitive overlap of ADHD and anxiety symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it