Firefighter preferences regarding post-incident intervention
Bibliographic record
Abstract
The effectiveness of Critical Incident Stress Debriefing (CISD) as a tool remains, at best, inconclusive. Yet in many locales CISD is mandatory for emergency services workers, including firefighters. To our knowledge, to date no study has investigated firefighters’ preferences for psychological intervention following traumatic events. To examine this, a survey was conducted with 142 members (54%) of an urban fire and rescue service in south-western Ontario, Canada. Firefighters were provided with five scenarios of varying traumatic intensity, for which they rated desirability of four voluntary post-incident interventions: CISD, individual debriefing, informal discussion, and no intervention. Firefighters expressed interest in working with post-event reactions within their peer group for all events, and an increasing interest in formal intervention as event severity increased. Individual debriefing was preferred to CISD in scenarios of low to moderate intensity. For scenarios of high intensity, ratings for all interventions were high. Expected relationships with prior CISD experience and years of service were not upheld. The essential role of informal peer-support, and the desire for meaningful intervention in severe situations, are discussed.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".