Associations between individual cognitive factors, mode of exposure and depression symptoms in practitioners working with aversive crime material
Bibliographic record
Abstract
Background: There is extensive literature on front-line officers and investigators exposure to trauma and its negative impact on them. However, there are analytical practitioners in law enforcement who indirectly work with the traumatic experiences of other people daily, but are seldom the focus of academic research.Objective: Our goal was to conduct the first international study with these practitioners to identify the risk of depression symptoms and establish whether potentially modifiable risk factors (belief in a just world, mental imagery and thought suppression) and work-related characteristics (medium of exposure) are associated with depression.Method: 99 analysts and secondary investigators employed in police and law enforcement organizations from the UK, Europe and Canada participated in the study. The online survey was advertised to employees via their employers but hosted without employer access. Multiple regression was used to analyze the data.Results: After controlling for age, gender, ethnicity, previous exposure to trauma, and marital status, four potential risk factors were identified. Analytical practitioners with vivid mental imagery, those exposed to crime material via auditory and visual means, those who suppressed intrusive thoughts, and those who believed in a just world reported more depressive symptoms.Conclusions: The majority of our sample reported clinical levels of depressive symptoms. Four potential risk factors accounted for just under half of the variance in depression scores. We consider strategies that can be used to mitigate the potential negative influence of these factors and suggest that these are established as risk factors for depression symptoms via future longitudinal research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".