Excel data coding file for child welfare reform scoping review
Bibliographic record
Abstract
While there have been ongoing calls to reform child welfare so that it better meets children’s and families’ needs, to date there have been no comprehensive summaries of child welfare reform strategies. For this systematic scoping review, we summarized authors’ recommendations for improving child welfare. We conducted a systematic search (2010 to 2021) and included published reviews that addressed authors’ recommendations for improving child welfare for children, youth, and families coming into contact with child welfare in high-income countries. A total of 4,758 records was identified by the systematic search, 685 full-text articles were screened for eligibility, and 433 reviews were found to be eligible for this scoping review. Reviews were theoretically divided, with some review authors arguing for reform efforts at the macro level (e.g., addressing poverty) and others arguing for reform efforts at the practice level (e.g., implementing evidence-based parenting programs). Reform efforts across socioecological levels were summarized in this scoping review. An important next step is to formulate what policy solutions are likely to lead to the greatest improvement in safety and well-being for children and families involved in child welfare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.040 | 0.049 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.407 | 0.042 |
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".