Self Efficacy in Depression
Bibliographic record
Abstract
We investigated the discrepancy between competence and real-world performance in major depressive disorder (MDD) for adaptive and interpersonal behaviors, determining whether self-efficacy significantly predicts this discrepancy, after considering depressive symptoms. Forty-two participants (Mage = 37.64, 66.67% female) with MDD were recruited from mental health clinics. Competence, self-efficacy, and real-world functioning were evaluated in adaptive and interpersonal domains; depressive symptoms were assessed with the Beck Depression Inventory II. Hierarchical regression analysis identified predictors of functional disability and the discrepancy between competence and real-world functioning. Self-efficacy significantly predicted functioning in the adaptive and interpersonal domains over and above depressive symptoms. Interpersonal self-efficacy accounted for significant variance in the discrepancy between interpersonal competence and functioning beyond symptoms. Using a multilevel, multidimensional approach, we provide the first data regarding relationships among competence, functioning, and self-efficacy in MDD. Self-efficacy plays an important role in deployment of functional skills in everyday life for individuals with MDD.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".