Overcoming Barriers to Peace: Mediating Violent, Ethnic, Intra-State Crises in Africa
Bibliographic record
Abstract
Reports an error in "Reward processing and future life stress: Stress generation pathway to depression" by Daniel M. Mackin, Roman Kotov, Greg Perlman, Brady D. Nelson, Brandon L. Goldstein, Greg Hajcak and Daniel N. Klein (<i>Journal of Abnormal Psychology</i>, 2019[May], Vol 128[4], 305-314). In the article, there is an error in Figure 2 in which the path from "T1-T3 Dependent Life Stress" to "T3 Depression" incorrectly includes a minus sign in front of the estimate value 0.25. Thus, the estimate should be positive and read as follows: <i>b</i> =0.25, [0.168, 0.330]. This error does not change the explanation of the results reported in the "Indirect effects of the RewP on depression" section of the Results or any conclusions outlined in the article. (The following abstract of the original article appeared in record 2019-23604-003.) Blunted reward sensitivity and life stress are each depressogenic. Additionally, individuals with clinical and psychosocial vulnerabilities are prone to experience or evoke dependent life stressors (e.g., interpersonal conflict) that, in turn, increase depression risk. However, no previous study has investigated the role of neural vulnerability factors in generating life stress. Therefore, the current study investigated whether a neural measure of reward sensitivity prospectively predicts the generation of life stress, which in turn mediates effects of these neural processes on subsequent depression. Participants were 467 never-depressed adolescent girls. Using event-related potentials, neural sensitivity to the difference between monetary reward and loss (the Reward Positivity [RewP]) was assessed at baseline. Negative life events were assessed twice via interview over the ensuing 18 months, yielding an index of total life stress over the follow-up period. A self-report dimensional measure of depression symptoms was administered at baseline and follow-up. After accounting for baseline age, depression, and race, a blunted RewP predicted greater dependent, but not independent, life stress over the follow-up. Mediation analyses revealed a significant indirect effect of the RewP on follow-up depression through dependent, but not independent, life stress. Our results suggest that neural processing reward and loss plays a crucial role in depressogenic stress generation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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".