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Triggered: Qualitatively exploring structural and social drivers of firearm violence exposure among LGBTQ+ young adults of color in Detroit

2025· article· en· W4413920213 on OpenAlexaff
Wesley M. King, Ini-Abasi Ubong, Dior’ Monro, K. Scott, Sydney N Strunk, J. Stephenson, Laura Jadwin‐Cakmak, Avery Everhart, Kristi E. Gamarel

Bibliographic record

VenueSocial Science & Medicine · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Child Health and Human DevelopmentCenters for Disease Control and PreventionUniversity of Michigan
KeywordsPoison controlInjury preventionSuicide preventionOccupational safety and healthCriminologyHuman factors and ergonomicsMedical emergencySociologyPsychologyEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

Firearm violence is a leading cause of injury and death among youth and young adults in the U.S. with notable inequities across race and ethnicity, geography, and gender. Lesbian, gay, bisexual, transgender, and queer (LGBTQ+) young adults are largely absent from firearms research. Guided by structural violence and Social Safety theory, we qualitatively explored structural and social influences on firearm violence exposure among LGBTQ+ young adults of color in Detroit, Michigan. Through analysis of in-depth interviews with 24 participants, we developed three themes aligned with this aim. First, participants' accounts reflected how contemporary and historical structural racism in Detroit is the root cause of the firearm violence. Second, participants characterized firearms as a source of protection in the absence of structural safety. Finally, participants described how firearm violence against LGBTQ+ people is often an attempt to regain social status lost to structural violence. These themes indicate that structural racism in Detroit has unique impacts on LGBTQ+ young adults of color's exposure to firearms and firearm violence. Future research with this community is needed to guide protective interventions and policy changes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.010
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.393
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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