I. INTRODUCTION
Bibliographic record
Abstract
Although comprehensive and ecological approaches to early childhood prevention are commonly advocated, there are few examples of long-term follow-up of such programs. In this monograph, we investigate the medium- and long-term effects of an ecological, community-based prevention project for primary school children and families living in three economically disadvantaged neighborhoods in Ontario, Canada. The Better Beginnings, Better Futures (BBBF) project is one of the most ambitious Canadian research projects on the long-term impacts of early childhood prevention programming to date. Bronfenbrenner's ecological model of human development informed program planning, implementation, and evaluation. Using a quasi-experimental design, the BBBF longitudinal research study involved 601 children and their families who participated in BBBF programs when children were between 4 and 8 years old and 358 children and their families from sociodemographically matched comparison communities. We collected extensive child, parent, family, and community outcome data when children were in Grade 3 (age 8–9), Grade 6 (age 11–12), and Grade 9 (age 14–15). The BBBF mandate was to develop programs that would positively impact all areas of child's development; our findings reflect this ecological approach. We found marked positive effects in social and school functioning domains in Grades 6 and 9 and evidence of fewer emotional and behavioral problems in school across the three grades. Parents from BBBF sites reported greater feelings of social support and more positive ratings of marital satisfaction and general family functioning, especially at the Grade 9 follow-up. Positive neighborhood-level effects were also evident. Economic analyses at Grade 9 showed BBBF participation was associated with government savings of $912 per child. These findings provide evidence that an affordable, ecological, community-based prevention program can promote long-term development of children living in disadvantaged neighborhoods and produce monetary benefits to government as soon as 7 years after program completion.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.414 | 0.297 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".