Evaluation of Life Events in Major Depression: Assessing Negative Emotional Bias
Bibliographic record
Abstract
BACKGROUND: Overly negative appraisals of negative life events characterize depression but patterns of emotion bias associated with life events in depression are not well understood. The goal of this paper is to determine under which situations emotional responses are stronger than expected given life events and which emotions are biased. METHODS: Depressed (n = 16) and non-depressed (n = 14) participants (mean age = 41.4 years) wrote about negative life events involving their own actions and inactions, and rated the current emotion elicited by those events. They also rated emotions elicited by someone else's actions and inactions. These ratings were compared with evaluations provided by a second, 'benchmark' group of non-depressed individuals (n = 20) in order to assess the magnitude and direction of possible biased emotional reactions in the two groups. RESULTS: Participants with depression reported greater anger and disgust than expected in response to both actions and inactions, whereas they reported greater guilt, shame, sadness, responsibility and fear than expected in response to inactions. Relative to non-depressed and benchmark participants, depressed participants were overly negative in the evaluation of their own life events, but not the life events of others. CONCLUSION: A standardized method for establishing emotional bias reveals a pattern of overly negative emotion only in depressed individuals' self-evaluations, and in particular with respect to anger and disgust, lending support to claims that major depressives' evaluations represent negative emotional bias and to clinical interventions that address this bias. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".