Social Work Education and Public Child Welfare: A Review of the Peer-Reviewed Literature on Title IV-E Funded Programs
Bibliographic record
Abstract
Collaboration between public child welfare agencies and social work education is not a new strategy. The relationship was kindled in the 1909 White House Conference and enshrined in Title IV-E support for social work education for public child welfare workers (Adoption Assistance and Child Welfare Act of 1980 1979. Retrieved from http://www.govtrack.us/congress/bills/96/hr3434H.R. 3434—96th Congress: Adoption Assistance and Child Welfare Act of 1980 [Google Scholar]). Federal support for the preparation of social workers in the field of child welfare can be traced as far back as 1935 with the inception of the Child Welfare Provisions of the Social Security Act (Zlotnik, 2002 Zlotnik, J. L. 2002. Preparing social workers for child welfare practice: Lessons from a historical review of the literature. Journal of Health & Social Policy, 15(3/4): 5–21. [Crossref], [PubMed] , [Google Scholar]). Although IV-E program evaluation research remains small, the contributions of existing studies have added to the field. This review highlights those contributions and accentuates the need to improve research efforts in terms of designs rigor, including sample size, power, effect size, instrumentation, analyses, and outcomes. In addition, it underscores the need to move forward in connecting outcomes to families and children by focusing on safety, permanence, and well-being.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".