Identifying risk of emotional sequelae after critical incidents
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Ambulance workers could benefit from a method for early identification of incidents likely to result in long-term emotional sequelae. There is evidence that persistence of some measures of anxiety beyond the first week after an incident is associated with sequelae. In this study we test the hypothesis that persistence of self-identifiable components of the acute stress reaction as early as a few days post-incident is associated with sequelae. METHOD: 228 ambulance workers volunteered to complete surveys on occurrence and persistence of physiological, behavioural and emotional responses to an index critical incident in the past, as well as symptoms of depression, post-traumatic stress, somatisation and burnout at the time of the survey. Data were analysed for associations between duration of each reaction and present symptoms. Using cut-off scores for the outcomes, we tested the RR of high scores in each of three situations: occurrence of the reaction, persistence of reaction beyond one night and persistence beyond 1 week. RESULTS: Prolonged duration of all five acute stress reaction components was associated with all four outcomes, with the strongest associations being with post-traumatic stress and depression symptoms. The occurrence of physical symptoms of arousal is an immediate predictor of long-term sequelae. Three other components--disturbed sleep, irritability and social withdrawal--provide potential indicators of long-term emotional sequelae as early as 2 days post-incident. CONCLUSION: Four easily identifiable responses to a critical incident can potentially be used for early self-identification of risk of later emotional difficulties. These findings should be submitted to prospective testing.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".