Engaging From Both Sides: Facilitating a Canadian Two-Generation Prenatal-to-Three Program for Families Experiencing Vulnerability
Bibliographic record
Abstract
BACKGROUND: Young children living in families experiencing social vulnerability, including low income, mental illness, addictions, social isolation, and/or homelessness, are at risk of developmental delay. Two-generation programs can improve outcomes for preschool children, but underlying mechanisms and outcomes for younger children remain unclear. PURPOSE: We explored program facilitation and identified developmental benefits of a two-generation program beginning prenatally. METHODS: 100) development between program intake and exit as measured by the Ages and Stages Questionnaires 3rd edition. RESULTS: Our core category, Engaging From Both Sides, included (a) Mitigating Adversity (focused codes Developing Trust, Letting Go of Fear, and Putting in the Effort); (b) Continual Learning (focused codes Staying Connected, and Taking it to the Community); (c) Fostering Families (focused codes Cultivating Optimism, and Happiness and Love); (d) Unravelling Cycles of Crisis (focused codes Advocating, and Helping Parents' Parent); and (e) Becoming Mainstream (focused codes Knowing Someone Has Your Back, and Managing Stress, Anxiety, and Anger). We found significant improvements in child Fine Motor, Problem-Solving, and Personal-Social domains between program intake and exit. CONCLUSIONS: Our study adds to existing literature regarding mechanisms of two-generation programs beginning prenatally. Mitigating effects of intergenerational adversity was the primary motivation for interaction and engagement of staff and parents in two-generation programming, which improved child development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".