Intimate partner violence, depression, and chronic low‐grade inflammation among middle‐aged women in Cebu, Philippines
Bibliographic record
Abstract
OBJECTIVES: Recent discussions in human biology have highlighted how local ecological contexts shape the relationship between social stressors and health across populations. Chronic low-grade inflammation has been proposed as a pathway linking social stressors to health, with evidence concentrated in high-income Western contexts. However, it remains unclear whether this is an important pathway in populations where prevalence is lower due to lower adiposity and greater infectious exposures. To investigate this further, we tested associations between multiple types of intimate partner violence (IPV), a highly prevalent stressor and health crisis globally, and C-reactive protein (CRP), a commonly used measure of chronic low-grade inflammation, in Cebu, Philippines. For reference, we compared results for CRP to depression, a well-established and consistently observed health outcome of IPV. METHODS: Data came from 1601 currently partnered women (ages 35-69 years) as part of the Cebu Longitudinal Health and Nutrition Survey. IPV exposures included physical, emotional, and controlling behavior. Depression scores were measured using a modified version of the Center for Epidemiologic Studies-Depression Scale for this population, whereas plasma CRP was measured from overnight-fasted morning blood samples. RESULTS: All three types of IPV were associated with a higher depression score. However, none of the IPV measures were associated with CRP. In a post hoc interaction test, emotional IPV became positively associated with CRP as waist circumference increased above the mean. CONCLUSIONS: Our results suggest a complex relationship between social stressors and chronic low-grade inflammation, which is likely dependent on the population-specific context of lifestyle and environmental factors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".