The Relationship Between Programming After Critical Incidents, Shootings, and Resilience in Police
Bibliographic record
Abstract
AbstractThe purpose of this study was to examine whether there was a relationship between resilience, posttraumatic growth, and reintegration programming after a critical incident and/or line of duty shooting through the cognitive, self-efficacy and resiliency theoretical lenses. The research aimed to determine if police officers, who participated in reintegration programming, specifically in this study, Edmonton Police’s Reintegration After Critical Incident programming, produced higher scores in resilience as measured on the Connor-Davidson Resilience Scale (CD-RISC) and posttraumatic growth, as measured on the Post Traumatic Growth Inventory scale (PTGI), with Canadian police officers compared to police officers who do not participate in this programming. A total of 68 participants were assigned to each group; one group of 34 who had participated in Reintegration After Critical Incident programming subsequent to their critical incident and one group of 34 who did not participate in Reintegration After Critical Incident programming subsequent to their critical incident. Using a comparative design, two separate One Way ANOVAs, determined that there was statistical significance in the relationship between resilience and participation in Reintegration After Critical Incident programming. This research determined there was no statistical significance between posttraumatic growth and Reintegration After Critical Incident programming. Implications for positive social change are that Reintegration After Critical Incident programming may prevent serious mental health issues through higher resilience in police officers after experiencing a critical incident and/or line of duty shooting should this programming be implemented in policing organizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".