Impacts of Organisational, Role and Environmental Factors on Moral Injury amongst Police Investigators in Internet Child Abuse Teams
Bibliographic record
Abstract
In the United Kingdom (UK), half of those police officers taking sickness leave in the last 5 years have done so because of mental health-related illness (Police Firearms Officer Association 2017). This situation is not confined to the UK. In the United States, more law enforcement officers have committed suicide than been killed in the line of duty in the last three consecutive years (Kamkar et al., 2019). Regular and repeated exposure to traumatic and critical events increase police officers’ susceptibility to mental health disorders, post-traumatic stress disorder (PTSD) depression and anxiety (Husain, 2014), and stress-induced diseases (Violanti et al., 2016a), with cases estimated to be at least four times higher than amongst the general population (Kates, 2008; Ombudsman Ontario, 2012). If left untreated, cumulative exposure to trauma leads to physiological stress-induced conditions which include; obesity, diabetes, higher rates of hypertension and raised cholesterol (Zimmerman, 2011), which combined, lead to rates of cardiovascular disease in police at 31.4% compared to 18.4% in the general population (Han, 2018). In addition, 40.4% of officers report a sleep disorder which effects their health, performance and safety (Garbarino, 2019). Viewed within the context of such individual mortality and morbidity, such mental and physical ill health adversely affects workplace performance, future career prospects (Heffren & Hausdorf 2016) and premature retirement (Collins and Gibbs 2003; Summerfield 2011), emphasising its importance as a focus for 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".