Alterations in positive affect: Relationship to symptoms, traumatic experiences, and affect ratings.
Bibliographic record
Abstract
OBJECTIVE: Intrusive negative affect and concurrent deficits in positive affect are hallmarks of posttraumatic stress disorder (PTSD). We sought to further extend the extant literature by exploring the experience of negative affect intrusion upon potentially positive situations (here termed, "negative affect interference," NAI). METHOD: Two studies with adults endorsing at least 1 traumatic event (Study 1, N = 294; Study 2, N = 286) examined how NAI and more general hedonic deficits (HD) relate to psychopathology, trauma exposure characteristics, and ratings of normed visual stimuli. RESULTS: Study 1 found that NAI and HD were positively correlated with PTSD symptoms and childhood trauma, and NAI incremented over depressive symptoms in predicting PTSD severity. Study 2 results indicated additional strong positive correlations between NAI and HD and anhedonia, affect regulation problems, negative affect, and neuroticism. NAI and HD were found to increment over trait NA in predicting PTSD symptoms. Individuals endorsing elevated NAI and HD rated positively valenced pictures (including food and erotic images) as less arousing, although not more negative. CONCLUSIONS: These findings expand conceptualizations of anhedonia and emotional numbing by drawing attention to negative affect in otherwise positive contexts. (PsycINFO Database Record
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".