Insecure attachment and posttraumatic stress disorder symptoms among Black, Indigenous, and People of Color first responders: The role of emotion dysregulation.
Bibliographic record
Abstract
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).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".