Advancing Early Childhood Development and Prevention Programs: A Pan‐Canadian Knowledge Transfer Initiative for Better Beginnings, Better Futures
Bibliographic record
Abstract
Better Beginnings, Better Futures is an evidence‐based early childhood initiative that promotes the healthy development of children and families in economically disadvantaged communities. The Better Beginnings model involves the development of community‐driven programs through resident involvement that are integrated with existing services, universally accessible to all children and families in a community, focused on prevention, and ecological in nature. In this paper, we describe a pan‐Canadian knowledge transfer strategy involving attendance at workshops designed to build the capacity of participants to develop Better Beginnings initiatives in their own communities, and the impacts of the workshops on participants. Process evaluation data were obtained from participants in each Canadian province and territory immediately following the workshops (k = 12, N = 271). In addition, a smaller sample (n = 54) completed a follow‐up telephone interview 3 months later. All components of the knowledge transfer strategy were well‐received and rated highly by participants. Workshop participants indicated increased knowledge of the Better Beginnings model and high levels of satisfaction with the workshop. In the follow‐up evaluation, participants indicated strong intentions to use the information they had learned about Better Beginnings to advance the well‐being of children and families in their community. We discuss the lessons learned from the knowledge transfer initiative and provide recommendations for advancing knowledge transfer of programs for children and families in context.
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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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".