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 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 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".