Examining the Empathy Profiles and Work Outcomes of Trauma Workers
Bibliographic record
Abstract
Trauma work involves intervening with others enduring acute pain and suffering, often with heavy psychological and physical health impacts. An important question is whether dispositional empathy helps or hurts trauma workers in their occupational functioning. The current study addresses this gap in the research literature by using a person-centered approach to examine the empathy profiles and professional outcomes of a broad sample of trauma workers ( n = 315). We measured their trait empathy and organizational outcomes (occupational burnout, person-job fit, turnover intentions, job performance), and found three distinct empathy profiles which differed significantly in their occupational functioning. A ‘self-focused’ empathy profile (dominated by high personal distress responding) reported the worst functioning; an ‘other-oriented’ profile (high on perspective taking and empathic concern) had more positive functioning, and an unexpected ‘low reactivity’ profile (a full SD below the general population on empathy facets) showed the lowest exhaustion. Exploratory analyses revealed that first responders (e.g., police, firefighters, EMTs, paramedics) were overrepresented in the ‘low reactivity’ profile, while psychology-related professions (e.g., psychologists, counsellors, social workers) were underrepresented in that profile. The significance of these results, as well as their implications for empathy research and vocational counselling in the field of trauma work, are discussed.
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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 | 0.001 | 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 | 0.000 |
| 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".