Development of the Behavioural-Biomedical Law Enforcement Stress Discordance Model (B2LESD): An epidemiological criminology framework (LEPH2018)
Bibliographic record
Abstract
The stressors associated with the law enforcement profession have become a focal point of discussion as the reporting of police misconduct has been increasing. Simultaneously researchers are exploring the relationship between police stress, as manifested through physical behavior, and health outcomes. While the current definitions and theories shed some light on the pathways of police stress leading to police misconduct, the emergence of more critical, interdisciplinary theories is essential and needed so as to better understand its underlying causes scientifically and practically. Relevant studies conducted from year 2008 to present were searched and collected, through a number of databases, to investigate the relationship between stress and police misconduct. The results of the final sample of ten studies were utilized to refine a conceptual model that serves as a guiding framework to more accurately provide a conceptual picture of police stress-exposure and the role of the bio-psycho-social and environmental contributors that impact the police work environment, thereby influencing the stress experienced by police officers that lead to police misconduct. We use the Epidemiological Criminology framework to understand the biobehavioural impact of stressful exposure on health and wellness of law enforcement officers. This framework intends to help the law enforcement, research, policy, and practice community to understand more effectively the bio-psycho-social and environmental health effects within the context of the behavioural and biomedical disparities of police officers, who are likely to experience high levels of stress while on duty—leading to the development of stress-reduction interventions for police officers.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".