A study protocol for community implementation of a new mental health monitoring system spanning early childhood to young adulthood
Bibliographic record
Abstract
Findings from longitudinal research, globally, repeatedly emphasise the importance of taking an early life course approach to mental health promotion; one that invests in the formative years of development, from early childhood to young adulthood, just prior to the transition to parenthood for most. While population monitoring systems have been developed for this period, they are typically designed for use within discrete stages (i.e., childhood or adolescent or young adulthood). No system has yet captured development across all ages and stages (i.e., from infancy through to young adulthood). Here we describe the development, and pilot implementation, of a new Australian Comprehensive Monitoring System (CMS) designed to address this gap by measuring social and emotional development (strengths and difficulties) across eight census surveys, separated by three yearly intervals (infancy, 3-, 6-, 9- 12-, 15-, 18 and 21 years). The system also measures the family, school, peer, digital and community social climates in which children and young people live and grow. Data collection is community-led and built into existing, government funded, universal services (Maternal Child Health, Schools and Local Learning and Employment Networks) to maximise response rates and ensure sustainability. The first system test will be completed and evaluated in rural Victoria, Australia, in 2022. CMS will then be adapted for larger, more socio-economically diverse regional and metropolitan communities, including Australian First Nations communities. The aim of CMS is to guide community-led investments in mental health promotion from early childhood to young adulthood, setting secure foundations for the next generation.
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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 | 0.073 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.146 | 0.046 |
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".