“Healthy Start.” A National Strategy for Parents With Intellectual Disabilities and Their Children
Bibliographic record
Abstract
Abstract Parents with intellectual disabilities, like all other parents, need support with child rearing. Often this support comes from family and friends, but in the case of parents with intellectual disabilities, they are more likely to have to rely on the service system. Research from a number of countries demonstrates that there is limited system capacity to support these parents. There are few appropriate services, and practitioners are generally ill‐equipped to meet the parents' particular learning and support needs. In response, the Australian government has funded a capacity‐building model known as Healthy Start: A national strategy for children of parents with intellectual disabilities, as part of its Stronger Families and Communities Strategy. This paper presents this model for building systems capacity that, in brief, addresses on the one hand, practitioner commitment, knowledge, and skills, and on the other, the parent education and community development resources needed to support parents with intellectual disability and promote a healthy start to life for their young children. The model involves the development of local leaders and practitioner networks in addition to dissemination of knowledge and innovation to support evidence‐based practice. Innovative, cross‐disciplinary, and inter‐sectoral practitioner networks are at the heart of this capacity‐building model. These networks bridge the gap between research knowledge and practitioner knowledge as a basis for planning and coordinating local service development.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".