How Significant is Partnership Formation in Area-Based Projects to Increase Parental Attendance at Maternal and Child Health Services?
Bibliographic record
Abstract
Objectives: In this study, we assess the importance of area-based partnerships in an initiative to improve access to Maternal and Child Health (MCH) services (known as Best Start) in socially disadvantaged communities in Victoria, Australia. Methods: The study assessed changes in MCH attendance rates, parental attitudes and local partnership formation before and after the introduction of Best Start projects. Partners involved in Best Start projects were surveyed regarding the extent of local partnership formation (before 54; after 84). Data was collected for MCH attendance using routine records for Best Start with MCH projects (before 1,739; after 1437) and the rest of the State (before 45,497; after 45,953). Two cross-sectional surveys of parents of 3-year old children were used to assess changes in parent’s knowledge about, and confidence in using relevant services as well as parental confidence more generally (before 1666; after 1838). Results: Best Start was significantly associated with improving: levels of partnership formation (5 of 7 relevant factors) attendance at the 3.5 year MCH visit in Best Start Sites with MCH projects between 2001/02-2004/05. parent’s access to information (partnership effect); confidence about attending the 3.5 year MCH visits (partnership effect); and overall parental confidence (project effect only). Conclusion: Best Start improves participation in the MCH attendance. This is related most directly to improving parent’s access to information and overall parental confidence either through local partnership or direct project effects.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".