Neurocognition and Depressive Symptoms have Unique Pathways to Predicting Different Domains of Functioning in Major Depressive Disorder
Bibliographic record
Abstract
BACKGROUND: Research has established the independent relationships between depressive symptoms to cognition and functioning in depression; however, little is known about the role of mediators in this relationship. We explored the role of neurocognitive abilities, depressive symptom severity, dysfunctional attitudes, and functional capacity in predicting two dimensions of daily functioning in individuals with major depressive disorder (MDD). METHODS: One hundred and twenty-four participants (mean age = 46.26, SD = 12.27; 56% female) with a diagnosis of MDD were assessed on a standard neurocognitive battery, self-reported depressive symptoms, dysfunctional attitudes, and clinician-rated functional impairment. They completed a performance-based assessment of functional competence. RESULTS: Confirmatory path analyses were used to model the independent and mediated effects of variables on two domains of functioning: social (relationships and social engagement) and productive (household and community activities). Cognition and depressive symptoms both predicted productive functioning, and dysfunctional attitudes mediated each of these relationships. Functional competence was a significant mediator in the relationship between neurocognition and productive functioning. Depressive symptoms and cognition were direct predictors of social functioning with no significant mediators. CONCLUSIONS: There are divergent pathways to different dimensions of daily functioning in MDD. Measurement implications include the consideration of multiple levels of predicting productive activities and more direct relationships with social outcomes. Treatments that directly target depressive symptoms and cognition might not generalize to improvements in everyday functioning if additional pathways to functioning are not addressed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".