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Record W4410402414 · doi:10.1037/tra0001946

Insecure attachment and posttraumatic stress disorder symptoms among Black, Indigenous, and People of Color first responders: The role of emotion dysregulation.

2025· article· en· W4410402414 on OpenAlexafffundabout
Ling Jin, Anjana Varadarajan, Ateka A. Contractor

Bibliographic record

VenuePsychological Trauma Theory Research Practice and Policy · 2025
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of Calgary
FundersGovernment of Alberta
KeywordsPosttraumatic stressPsychologyIndigenousClinical psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: First responders are at greater risk of developing posttraumatic stress disorder (PTSD) due to constant exposure to potentially traumatic events. Studies have shown that both insecure attachment and emotion dysregulation contribute to more PTSD symptom severity. However, it is unclear whether emotion dysregulation explains relationships between insecure attachment and PTSD symptoms, especially among Black, Indigenous, and People of Color (BIPOC) first responders. METHOD: = 35.40; 71.80% men) residing in Canada completed research questionnaires. The direct and indirect effects of attachment insecurity (i.e., attachment anxiety and attachment avoidance) on PTSD symptom clusters (intrusions, avoidance, negative alterations in cognitions and mood, alterations in arousal and reactivity) via emotion dysregulation were examined via PROCESS macro Model 4. RESULTS: ² = 19.79% to 38.15%). CONCLUSIONS: Trauma-exposed BIPOC first responders with insecure attachment styles are more likely to experience difficulties regulating emotions, which increases the severity of all four PTSD symptom clusters. Culturally congruent, trauma-informed treatments may benefit from targeting emotion regulation among BIPOC first responders to improve posttrauma well-being. (PsycInfo Database Record (c) 2026 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)0.005
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.451
Teacher spread0.393 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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