The Role of Retrospective Negative Information-Processing Biases in Remitted Depression
Bibliographic record
Abstract
Major Depressive Disorder (MDD) is characterized by persistent and impairing low mood and loss of interest or pleasure. Given that MDD is highly recurrent, it is important to identify which impairments remain during remission and may predict recurrence. A key impairment in MDD is that they tend to process current and past information more negatively than healthy individuals. However, it is unclear whether this negative information-processing bias persists during remission. This study will investigate a retrospective type of negative information-processing bias when recollecting recent real-world events among young adults with remitted depression (n=31) compared to healthy individuals (n=32). Participants were given a handheld device and responded to prompts on the device four times a day for one week. The prompts asked whether the individual experienced a positive or negative event since the last prompt and how intense that negative or positive event was. At the end of the week, participants completed a questionnaire regarding their experiences over the past week. They were asked how many negative and positive events the individual experienced over the past week, and the overall intensity of these negative and positive events. It is hypothesized that individuals with remitted depression will report a greater number and intensity of negative events in the distal retrospection period than in the proximal retrospection period, but no difference is expected for positive events. The opposite findings are expected for healthy individuals. This research may advance the understanding of persistent impairments in remitted depression while focusing on real-life events.
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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 | 0.003 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".