Healing the Healer: Exploring Barriers and Solutions to Supporting Workers in the Domestic Violence Sector
Bibliographic record
Abstract
Individuals who work with Domestic Violence (DV) survivors are often exposed to traumatic events that can leave them feeling overwhelmed, distressed, and susceptible to experiences of trauma themselves. The purpose of this exploratory study was to understand the health and wellbeing of staff in the DV sector to build capacity around providing safe and supportive working environments. A focus group was conducted with 40 members of a local domestic violence collective while surveys were completed by 61 professionals within the DV sector. Thematic analysis of focus group discussions and descriptive analysis of survey data highlighted primary barriers to supportive and safe organizational cultures including the work environment, leadership, and supervision. Specifically, supervisors and organizational culture play a significant role in contributing to employee health and wellness. Results suggest the need for increased importance on the role that senior/executive staff must take in protecting their staff from trauma-related harms, including focusing on trauma-informed supervision, structure, self-care, education and training, agency policies and the safety of the work environment. Future research could explore the impact of prioritizing the role of senior and executive staff in creating a safe working environment while informing new policies and strategies for mitigating staff burnout.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".