Evidence-Informed Decision-Making Training in Child Welfare: Evaluation of a Proof of Concept
Bibliographic record
Abstract
Purpose Child welfare organizations serve vulnerable families and are required to effectively address the system’s dual mandate. Therefore, workers must understand how families’ unique challenges may impact caregiver’s parenting ability and how to mitigate these concerns. In turn, workers require a framework for service that will address clinical factors and reduce decision-making noise. Evidence-informed decision-making (EIDM) offers a comprehensive framework that guides child welfare workers through the service process.Method This study provides the evaluation of an EIDM training (n= 100) aimed at promoting attitudes and use of research of child welfare workers. This is a quantitative study that utilized pre- and posttest surveys to measure attitudes and behaviors related to EIDM.Results Findings suggest that attitude toward and likelihood to adopt EBP, confidence in using research, and perceived barriers to utilizing research significantly improved over the course of the five-month training.Discussion Findings suggest that key participant characteristics can be improved following education. Indeed, there are many factors, including organizational, that contribute to whether EIDM is utilized in the field. Workers, however, must be knowledgeable and feel confident about the use of EIDM in everyday practice for there to be a successful implementation and sustained use in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".