Bibliographic record
Abstract
Critical Incident Stress Management (CISM) is offered by many EAP companies to assist employees deal with their emotional response to a critical event. Some organizations utilize employees in this process while others focus on the work of the mental health professional. A current model (Justice Institute of British Columbia) for CISM utilizes both mental health professionals and peers in CISM response and this is the model utilized by Family Services EAP within Canada Post Corporation Pacific Region. The use of employees (peer diffusers) in critical incident response has its locus within the fire, police, and ambulance services (Mitchell, 1988) and is well established within these groups as a requirement of the CISM process. The focus of this study was to develop a system of intervention utilizing peer diffusers to assist the employees of Canada Post Corporation Pacific Region who experience emotional trauma due to a significant or traumatic event. The results of this applied research exhibited the value of peer diffuser intervention in the quick emotional recovery of employees. It also met our primary purpose of demonstrating validity for the involvement of peer diffusers in this workplace. An unexpected result was how the peer diffusers reduced lost employee time, were able to transfer skills to other employee workplace issues enhancing employee resilience and how their involvement in the workplace improved organizational culture and workplace health.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".