Coping and Protective Factors of Mental Health: An Examination of African American and US Caribbean Black Women Exposed to IPV from a Nationally Representative Sample
Bibliographic record
Abstract
Existing research continues to highlight the harm that intimate partner violence (IPV) can pose to health and well-being. However, little is done to understand the effectiveness of coping and protective mechanisms in helping women manage under adverse circumstances. The current study addresses the mental health of US Black women and the role of coping and protective moderators. An analysis of data from the National Survey of American Life (2001-2003), the most comprehensive survey on the health of US Blacks, was conducted. The association between severe physical intimate partner violence and mental health outcomes were confirmed. Resilience moderated the relationship between severe intimate partner violence and mood disorder among US Black women, but this differed between African American and Caribbean Blacks. Resilience, emotional family support, and spirituality reduced the likelihood of having a mental health condition for some African American and Caribbean Black women, while the opposite was found for religiosity. Demographic factors were also associated with mental health conditions and behaviors. The study draws our attention to potential coping and protective mechanisms that could be incorporated into counseling and intervention practices while recognizing factors that may be harmful to the mental health of individuals.
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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 | 0.001 |
| 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 | 0.001 | 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".