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Record W4228998594 · doi:10.1037/tra0001246

Development and validation of the Inventory of Stress and Coping for African American Survivors of Homicide Victims.

2022· article· en· W4228998594 on OpenAlexaff
Tanya L. Sharpe, Derek K. Iwamoto, Kerry A. Lee, Jheanelle M Anderson

Bibliographic record

VenuePsychological Trauma Theory Research Practice and Policy · 2022
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHomicidePsychologyCoping (psychology)PsycINFOClinical psychologyPoison controlConstruct validitySuicide preventionMedicinePsychometricsMedical emergencyMEDLINE

Abstract

fetched live from OpenAlex

OBJECTIVE: African Americans disproportionately experience homicide. However, validated measures designed to assess the traumatic impact of coping with murder for surviving family members and friends of homicide victims are absent from research. This article describes four studies that contributed to the development and preliminary validation of the Inventory of Stress and Coping for African American Survivors of Homicide Victims (ISCASHV). The ISCASHV is a measure that assesses multicomponents (e.g., stress, appraisals, stigma, coping) of the sociocultural and psychological processes in which African Americans cope with homicide. METHOD AND RESULTS: Studies 2-4 provide strong support for the multicomponent 42-item scale consisting of five factors: cultural trauma, reactions to homicide, culture of homicide, racial appraisal, and coping. The ISCASHV demonstrates strong construct validity evidence and good internal consistency estimates. Each of the five measures demonstrated a high test-retest estimate for a 2-week period, suggesting the temporal stability of the factors. CONCLUSIONS: Results suggest that the ISCASHV is a promising multicomponent measure that assesses the racial- and sociocultural-bound manifestation of homicide-related grief for African American survivors of homicide victims. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.201
GPT teacher head0.501
Teacher spread0.299 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2022
Admission routes1
Has abstractyes

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