A public health approach to promoting better mental health outcomes for children of parents with a psychiatric disability.
Bibliographic record
Abstract
TOPIC: This paper describes the development of a national initiative to promote better mental health outcomes for children of parents with a psychiatric disability. PURPOSE: The purpose of the paper is twofold. First, the paper reflects on the policy framework and factors that led to the development of the national initiative and its outcomes. Secondly, the paper is intended to provide information about strategies utilized in the initiative that may be transferable to other settings. SOURCES USED: Health and social policy literature and personal observations are the major sources used. CONCLUSIONS: National public health policy concepts of the promotion of positive health and well-being and the prevention of illness have been a valuable vehicle for increasing awareness of, and responses to, the mental health of Australian children, parents, and families where a parent has a psychiatric disability. The involvement of young people and families in the development of policies and service guidelines that impact upon their lives, in partnership with professionals, government and non-government organizations, warrants continued emphasis. Translating policy into practice requires much ongoing work that can be supported by coalition building, development of a sound research and evidence base, and effective workforce education.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".