Preventing placement breakdowns in child welfare with a foster parent pre-service training program
Bibliographic record
Abstract
This study explores the need for a pre-service training program for Manitoba’s foster parents for the purposes of preparing and equipping foster parents with the skills necessary to respond to children with complex needs and to create stable placements for children in care. Current literature has found an increase in stable placements and fewer breakdowns when foster parents engage in a pre-service training program. Despite this, Manitoba is one of the only jurisdictions in the world that does not require such training. A total of 13 participants from three different groups (child welfare professionals, foster parents, and previous children in care) were interviewed using a semi-structured, one-on-one interview style. Both critical and standpoint theories were used as guiding theoretical frameworks and mainstream qualitative research design was used for data analysis. Significant findings include that all 13 participants supported a pre-service training program in Manitoba. Most foster parents in this study Manitoba felt unsupported and unprepared for their roles as caregivers to children with complex needs. This finding was corroborated by child welfare professionals, and from previous children in care. The participants provided training recommendations, including communication and documentation, a more comprehensive orientation, a systems training, and child-focused training.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".