Identifying, describing, and expressing emotions after critical incidents in paramedics
Bibliographic record
Abstract
For paramedics, critical incidents evoke intense emotions and may result in later psychological difficulties. We examined 2 ways to deal with emotions after critical incidents: (a) identifying emotions, and (b) describing and expressing emotions, and their association with recovery from acute stress and psychological symptoms. We surveyed 190 paramedics, examining how impaired capacity to identify and describe emotions (alexithymia) and voluntary expression of emotions during contacts with others in the first 24 hours after the incident were associated with recovery from acute stress and current symptoms of PTSD, depression, burnout, and somatization. Overall alexithymia was not associated with recovery, but the component of difficulty identifying feelings was associated with prolonged physical arousal (χ(2) = 10.1, p = .007). Overall alexithymia and all its components were associated with virtually all current symptoms (correlation coefficients .23-.38, p < .05). Voluntary emotional expression was unrelated to current symptoms. Greater emotional expression was related to greater perceived helpfulness of contacts (χ(2) = 56.8, p < .001). This suggests that identifying emotions may be important in managing occupational stress in paramedics. In contrast, voluntary emotional expression, although perceived as helpful, may not prevent symptoms. These findings may inform education for paramedics in dealing with stress.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".