Evidence-Informed Decision Making in Child Welfare: A Randomized Control Trial Evaluation
Bibliographic record
Abstract
Purpose Child welfare practice often requires direct intervention with vulnerable children and families, whereby workers are responsible for various services and decisions that may have a lasting impact on families involved in the child welfare system. Research illustrates that clinical needs are not necessarily the only factor at the foundation of decision making; Evidence-informed Decision Making (EIDM) can act as a foundation for critical thinking and deliberate practice in the context of child welfare service delivery. This study evaluates an EIDM training that aimed to enhance workers’ behavior and attitude toward the EIDM process with a focus on research.Method This randomized control trial evaluated the effectiveness of an online EIDM training for child welfare workers. The training consisted of five modules that were completed at the team (n = 19) level at a rate of approximately one module every three weeks. The training intended to promote the exploration and use of research in everyday practice by critically thinking through the EIDM process.Results Due to attrition and incomplete posttests, the final sample size was 59 participants (intervention, n = 36; control, n = 23). Generalized Linear Model Repeated Measures analyses found an EIDM training main effect on confidence in using research and research use.Discussion and Conclusion Importantly, findings suggest that this EIDM training can influence participant outcomes related to engaging in the process and the use of research in practice. Engagement with EIDM is one mechanism to promote critical thinking and exploration of research during the service delivery process.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".