Re-Imagining Child Welfare With Service Users: What Children's Social Workers Need to be Taught in School
Bibliographic record
Abstract
As social workers we understand that service users are the most impacted stakeholders involved in service delivery models at various agencies. When it comes to the field of child welfare, there are added barriers and complications that impact a worker’s ability to develop relationships with service users. What do child welfare service users consider to be “good” social work practice, and what do they expect from their workers? This thesis will focus on the voices of those who have been most impacted by the system: those who are or have been in the care of a child welfare system. At McMaster University, a program is being initiated in partnership between the School of Social Work and various local Children’s Aid Societies in Hamilton and the surrounding areas, which will explore how child welfare service users can be incorporated into the education of social work students who plan to work in the field of child welfare. This thesis will explore what individuals who are or have been youth in the care of an Ontario Children’s Aid Society want to teach the students of this program before they become child welfare social workers. This expert feedback will then be incorporated into the curriculum of McMaster’s program, entitled: “Preparing for Critical Practice in Child Welfare” (PCPCW), which will be carried into practice by the students who graduate from the program to become child welfare social workers.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.022 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".