Stress sensitivity and stress sensitization in psychopathology: An introduction to the special section.
Bibliographic record
Abstract
The goal of this special section is to examine the mechanisms of enhanced sensitivity and sensitization to stress as they influence the etiology and pathophysiology of psychopathology. The 12 articles in the section focus on some of the most crucial and unanswered questions regarding the underlying mechanisms and functional consequences of stress sensitivity and stress sensitization in psychopathology. They address the constructs of stress sensitivity and stress sensitization using state-of-the-art, and often novel, methodologies. The special section also focuses on an important terminological distinction between two related but distinct stress mechanisms that are often conflated. Individuals who are sensitive to stress possess this characteristic as a putative trait that develops through genetically mediated transactional relations between temperamental characteristics and the early contextual environment. In contrast, individuals who are sensitized to stress become so over time through repeated exposure to external, as well as endogenous, stressors. Enhanced stress sensitivity and sensitization have been included in conceptual models of psychopathology. Yet, the specific mechanisms by which these stress processes impact the onset and course of psychiatric disorders are not fully understood. These articles focus on several mechanistic accounts of stress sensitivity and sensitization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".