Assessing Secondary Trauma, Compassion Satisfaction, and Burnout – Implications for Professional Education for Asian-American Social Workers
Bibliographic record
Abstract
The present study explored work-related stress and career experiences of Asian-American social workers and assessed if their demographic characteristics, beliefs and orientations (altruism, idealism, and self-compassion), and work-related stressors might impact their professional quality of life (secondary trauma, compassion satisfaction, and burnout) and job-related health problems. Two hundred and eight (208) Asian social workers and students participated in a comprehensive online survey by providing basic demographic and work-related information and completing a set of standardized scales to assess their career experiences and work-related stress, as well as their psychological and physical well-being. Bivariate analyses and stepwise multiple regression analyses were used to estimate models that best predicted their experiences of secondary trauma, compassion satisfaction, burnout, perceived stress, and job-related health problems. The findings showed that higher perceived stress was associated with higher secondary trauma, burnout, job-related health problems, and lower compassion satisfaction. Work-related problems/stressors emerged as a very strong predictor of burnout and job-related health problems. Higher self-compassion was related to higher compassion satisfaction and lower secondary trauma and burnout. Self-compassion was found to be a very strong predictor of perceived stress. Implications of the findings for professional education and career development for Asian-American social workers were 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".