Investigating the cyclical hypothesis of client aggression as a ‘loss spiral’: Can child protection worker distress lead to more client aggression?
Bibliographic record
Abstract
Working in a stressful environment, child protection workers (CPWs) are often victims of psychological and physical acts of aggression perpetrated by their clients. This can be emotionally distressing for CPWs. Previous authors have suggested that this distress could place CPWs at greater risk for subsequent victimisation if they become emotionally unavailable to their clients. This study sought to investigate whether the distress experienced after an act of client aggression or other types of potentially traumatic events could indeed predict subsequent victimisation over time. Using cross-lagged panel analysis, researchers administered standardised questionnaires to 173 CPWs who had experienced an act of client aggression or other type of potentially traumatic event in the month prior. Participants were asked to fill out additional questionnaires 2, 6 and then 12 months later. Researchers found that CPW distress did in fact predict subsequent victimisation at the 2-month time point only. Researchers then conducted a generalised linear model analysis to test the influence of sociodemographic variables and the moderating influence of supervisor support. Supervisor support did not moderate the relationship between initial distress levels and increased aggression 2 months later. The study concludes by examining how the emotional distress of CPWs can negatively impact clients and how organisations can best provide support, in part, by rethinking aggression prevention.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".