Exploring Research on the Coping Strategies of Black Survivors of Homicide Victims: A Scoping Review
Bibliographic record
Abstract
Black communities are disproportionately impacted by homicide, yet research on how surviving family members and friends of murdered victims cope with their violent, traumatic death is limited. This scoping review examines current literature on how Black communities cope with homicide, identifies key concepts, reviews study methods, summarizes findings, and offers implications for future research. A scoping review of empirical studies (2000–2024) consisting of majority Black participants coping with homicide was conducted. Out of 2,932 articles, 35 met the inclusion criteria. An inductive thematic analysis was used to synthesize results. Findings from these studies revealed two domains: the sociocultural context of coping and coping strategies, consisting of eight themes (e.g., racism and social stigma, the impact of homicide, support, spiritual coping, activism, maintaining a connection to the deceased, substance use, avoidance, and concealment). These findings emphasize social and cultural factors that shape Black experiences with homicide, which consequently impact the coping resources and strategies they use to manage the violent, traumatic death of loved ones. Implications for future research should focus on standardized collection of homicide data, contextualization of homicide experiences, and diverse research methods.
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 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".