Exposure to violence and cardiovascular and neuroendocrine measures in adolescents
Bibliographic record
Abstract
BACKGROUND: Exposure to violence has clear, detrimental psychological consequences, but the physiological effects are less well understood. PURPOSE: This study examined the influence of exposure to violence on biological basal and reactivity measures in adolescents. METHODS: There were 115 high school student participants. Systolic and diastolic blood pressure (SBP, DBP), heart rate (HR), HR variability (HRV), and cortisol levels were recorded during baseline and a laboratory stressor. The Exposure to Violence interview was administered and assessed two dimensions: total observed violence and total personally experienced violence. These were then divided into component parts: lifetime frequency, proximity, and severity. RESULTS: Greater total experienced violence was associated with increased basal SBP (r = .19, p < .05) and decreased acute stress reactivity in terms of SBP (beta = -.13, p = .05), HR (beta = -.21, p = .00), and HRV (beta = .13, p = .05). Lifetime frequency of experienced violence was associated with higher basal DBP (r = .33, p < .05), HR (r = .33, p < .05), and cortisol (r = .53, p < .00), and decreased SBP (beta = -.27, p < .05) and DBP (beta = -.31, p < .05) reactivity. Exposure to violence is associated with increased biological basal levels in adolescents, supporting allostatic-load research and decreased cardiovascular reactivity, supporting the inoculation effect. CONCLUSIONS: The findings illustrate that being a victim of violence has more pervasive biological consequences than witnessing violence and that the accumulation of stressful experiences has the greatest effect on biological markers.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".