Piloting the Mockingbird Family™ in Australia: Experiences of foster carers and agency workers
Bibliographic record
Abstract
Abstract Given that the number of children and young people needing care keeps rising and fewer people are becoming foster carers, efforts to support carers and workers in foster caring are essential. This paper considers the experiences of carers and foster care agency workers involved in Australia's piloting of the Mockingbird Family. With a view understanding experience, data were collected via focus groups with carers and agency workers (n = 20) involved in piloting, implementation and evaluation. Deductive analysis applied the theory of experience to generate understanding of experience, as both intrinsic and extrinsic dimensions to capture strengths in the Mockingbird Family's foster caring networks. These dimensions of experience included collective passions of carers and workers; experiential change over time; collective experiences as a moving force; and experiences as transformational. Understanding of experience associated with the perceived strengths of the Mockingbird Family, including strategies to promote strong professional relationships between carers and workers, is an important element in strengthening environments of children and young people in care. Safe and stable environments are crucial for wellbeing.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".