Latent transition analysis on post-traumatic stress and post-traumatic growth among firefighters
Bibliographic record
Abstract
Background: Firefighters, in the course of their professional responsibilities, confront an array of stressors contingent upon the distinctive characteristics of their duties.Objective: This study investigated the longitudinal impact of trauma incidents during duty on firefighters using latent transition analysis.Method: Data from 346 firefighters in South Korea who had experienced trauma events while on duty were utilized. Initially, latent groups were identified based on the relationship between post-traumatic stress disorder (PTSD) and post-traumatic growth (PTG). Groups were labelled based on the analysis of differences in PTSD, mental health, and growth-related factors among classified groups. Subsequently, transition probabilities and patterns from Time 1 to Time 2 were examined, followed by an investigation into variances based on demographic factors (gender, age) and occupational factors (work experience, shift pattern) using variance analysis and multinomial logistic regression analysis.Results: First, at Time 2, a five-group model was classified into ‘Growth,’ ‘Resilience or Numbness,’ ‘Struggle,’ ‘Partial Struggle,’ and ‘PTSD’ groups. Second, upon examining the transition patterns between latent groups, four patterns emerged: ‘continued distress,’ ‘growth,’ ‘adaptation,’ and ‘escalated distress.’ Third, the ‘Struggle’ group showed a 0% probability of transitioning to the ‘Growth’ group, whereas it displayed the highest probability among the groups transitioning to the ‘PTSD’ group. Fourth, latent transition analysis results showed a strong tendency for the ‘Growth’ group and ‘Resilience or Numbness’ group to remain in the same category. Fifth, age was found to be a significant factor affecting the transition of latent groups.Conclusion: This research represents the inaugural attempt to longitudinally investigate the interplay between PTSD and PTG among firefighters.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".