Heterogeneity in racist events and posttraumatic mental health among Black, Indigenous, People of Color (BIPOC) first responders
Bibliographic record
Abstract
Background: Black, Indigenous, People of Color (BIPOC) first responders in Canada report experiencing racism and an increased risk of trauma-related mental health symptoms.Objective: Using a BIPOC first responder sample in Canada, the present study examined subgroups of BIPOC first responders based on the frequency of different types of racist events, and their relations with mental health symptoms (posttraumatic stress disorder [PTSD] symptom clusters of intrusion, avoidance, negative alterations in cognitions and mood [NACM], and alterations in arousal and reactivity [AAR]; depression severity; anxiety severity).Method: The sample included 196 BIPOC first responders who reported more than one traumatic experience (Mage = 35.30; 71.4% men).Results: Latent profile analyses indicated a best-fitting 3-profile solution: Low (Profile 1), Moderate (Profile 2), and High (Profile 3) Frequency of Racist Events. Multinomial logistic regression indicated that BIPOC first responders reporting more frequent racist events endorsed greater depression severity, anxiety severity, and PTSD’s NACM symptom severity.Conclusions: Findings improve our understanding of subgroups of BIPOC first responders based on the frequency and types of racist events they experience. Results highlight the need to incorporate assessments of racism-related experiences into therapeutic work, and to target depression, anxiety, and NACM symptoms among those who encounter more racist events.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".