Application of the Implicit Relational Assessment Procedure (IRAP) to Evaluate Bias Related to Intimate Partner Violence (IPV) within Child Welfare
Bibliographic record
Abstract
Problematic bias evidenced by child welfare professionals in relation to Intimate Partner Violence (IPV) victimization can negatively impact outcomes for children and families in the foster care system. The literature supports malleability of IPV-related bias in response to training interventions. These studies rely heavily on self-report measures. Self-report tools capture extended responses (explicit bias). These measures are less likely to reflect immediate responses (implicit bias). Combining explicit and implicit measures may inform a more comprehensive understanding. Purpose of Study: We employed a multi-method protocol to measure bias evidenced by dependency professionals in relation to IPV victimization. Method: Participants completed the Implicit Relational Assessment Procedure - Intimate Partner Violence (IRAP-IPV), an explicit analog of the IRAP-IV, and a gender-neutral version of the Domestic Violence Myth Acceptance Scale (GN-DVMAS). Principle Results: Results show expected divergence between explicit and implicit measures, with stronger positive valuation reflected on the explicit tools. We compared IRAP-IPV scores across in person and virtual groups. While statistical analyses indicate no significant between-group differences, divergence is evident upon visual inspection. Conclusion: This study supports the importance of multi-method measurement when evaluating IPV-related bias. We discuss results in terms of social and contextual factors within child welfare that may influence how dependency professionals respond to IPV. We offer recommendations for promoting a more equitable child welfare experience for victim-survivors, their families, and the professionals who serve them.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".