‘A big nebulous, multifaceted concept’: reflections from Victorian personal injury lawyers on wellbeing, burnout and vicarious trauma
Bibliographic record
Abstract
There is a spotlight on mental health, with government initiatives in Australia highlighting the importance of, and need for, greater focus on psychological wellbeing and on addressing psychosocial hazards at work. The growing body of evidence in Australia and internationally suggests that the mental health and wellbeing of lawyers is adversely affected by their work. This cross-disciplinary evidence highlights the need for mental health concerns to be addressed systemically to prevent psychosocial injury and for tailored, proactive psychological support services in the legal environment. In this article we present evidence derived from qualitative interviews with Victorian personal injury lawyers, which form part of a broader study of lawyers and mediators engaged in emotion-laden work. This study aimed to ascertain to what extent the legal system considers the emotional wellbeing and mental health needs of personal injury disputants, lawyers and mediators, identify ways to reduce stigma associated with help seeking and inform proactive prevention initiatives and tailored support services. Findings from this build on past research and continue to highlight themes around stigma, vicarious trauma and collegial support and call attention to the psychological impact of legal practice on Australian lawyers. From this, preventative measures can be developed and implemented to avoid psychosocial injury and provide much-needed specialised support services.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.035 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".