Building resiliency among law enforcement officers
Bibliographic record
Abstract
Law enforcement officers are frequently exposed to stressors such as organizational and operational stress. These sources of stress have the potential to manifest or cumulate over time and lead to various negative consequences for the officers. In addition to these sources of stress there are also critical incidents which can lead to trauma. Trauma also has several negative consequences for officers such as physical and mental health issues. If left untreated, these issues have the potential to manifest and develop into Post Traumatic Stress Disorder (PTSD). PTSD is a psychiatric disorder that can develop when individuals experience or witness a traumatic event (Parekh, 2017).\nPTSD can occur in any population or ethnicity and is prevalent in approximately 3.5% of adults in the United States according to Parekh (2017). The rate for law enforcement officers is much higher. Carleton, Afifi, Taillieu, Turner, Krakauer, Anderson, and McCreary (2019) found that approximately 44.5% of Canadian Public Safety Personnel screened positive for PTSD.\nFortunately, there are several protective factors that can mitigate the negative effects of PTSD for law enforcement officers. These include Critical Incident Stress Management, Strong and Effective Leadership, Peer Support, Mindfulness, Road to Mental Readiness, and Employee Assistance Programs.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".