VOICES FROM THE FIELD- Research on Home Visiting: Implications for Early Childhood Development (ECD) Policy and Practice across Canada
Bibliographic record
Abstract
Policy perspective There are now home visiting programs in virtually every jurisdiction across Canada.1 As in the United States, many of these programs emerged out of pressing policy needs to prevent child maltreatment. Policy-makers are continually challenged to make decisions based on limited evidence, frequently in the absence of rigorous evidence. For example, results from an early evaluation of the Hawaii Healthy Start program were sufficient for the Hawaii state legislature to implement the program across the state, despite the lack of a comparison group in the evaluation.2,3,4 This pattern of policy-making, which has been common in the history of home visiting, reflects an ongoing tension between advocacy and science.5,6 There is now considerable evidence on the effects of home visiting studied under optimal research conditions (i.e. efficacy),7,8,9,10 including the randomized trials and longitudinal follow-up studies of Olds, Kitzman and colleagues.11,12,13,14 More importantly for policy, there is also emerging evidence of the effects of home visiting studied under real-world conditions of service delivery (i.e. effectiveness), including the randomized trials of
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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 | 0.057 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.034 | 0.033 |
| Scholarly communication | 0.027 | 0.009 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 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".