Attachment Insecurity, Responses to Critical Incident Distress, and Current Emotional Symptoms in Ambulance Workers
Bibliographic record
Abstract
Ambulance workers are exposed to critical incidents that may evoke intense distress and can result in long-term impairment. Individuals who can regulate distress may experience briefer post-incident distress and fewer long-term emotional difficulties. Attachment research has contributed to our understanding of individual differences in stress regulation, suggesting that secure attachment is associated with effective support-seeking and coping strategies, and fewer long-term difficulties. We tested the effect of attachment insecurity on emotional distress in ambulance workers, hypothesizing that (1) insecure attachment is associated with symptoms of current distress and (2) prolonged recovery from acute post-critical incident distress, coping strategies and supportive contact mediate this relationship. We measured (1) attachment insecurity, (2) acute distress, coping and social contact following an index critical incident and (3) current symptoms of post-traumatic stress, depression, somatization and burnout and tested the hypothesized associations. Fearful-avoidant insecure attachment was associated with all current symptoms, most strongly with depression (R=0.38, p<0.001). Fearful-avoidant attachment insecurity was also associated with maladaptive coping, reduced social support and slower recovery from social withdrawal and physical arousal following the critical incident, but these processes did not mediate the relationship between attachment insecurity and current symptoms. These findings are relevant for optimizing post-incident support for ambulance workers.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".