An exploration and analysis on the timeliness of critical incident stress management interventions in healthcare.
Bibliographic record
Abstract
There is debate in the literature regarding the definition and effectiveness of "early" critical incident stress management (CISM) interventions. Definitions range from interventions that take place within twelve hours (Everly and Mitchell, 1999; Stallard, Velleman, Salter & Howse, 2005) and up to a three month window (Bisson & Cohen, 2006). Others define early support as an intervention directly after an incident, before the individual leaves work and definitely before having their first sleep (Talbot, 1990; Snelgrove, 2000). Most CISM research is carried out in industries that do not have the same characteristics as healthcare namely, 24/7 operation and a strong female demographic. Therefore, given the lack of research evidence around the timeliness of CISM interventions in healthcare, this study examined the effect of early (< 24 hours post-incident) vs. late (> 24 hours post-incident) CISM interventions on stress reaction and employees perceptions of service. Although the subject population in each group was too small to show statistical significance, the quantitative data showed an overall trend that the early intervention group had lower mean scores for avoidance, intrusion and hyperarousal at all three time periods. Thematic analysis demonstrated both groups found the CISM intervention was beneficial and the timing appropriate.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".