DISCOVERING FRUGAL INNOVATIONS THROUGH DELIVERING EARLY CHILDHOOD HOME‐VISITING INTERVENTIONS IN LOW‐RESOURCE TRIBAL COMMUNITIES
Bibliographic record
Abstract
Early childhood home-visiting has been shown to yield the greatest impact for the lowest income, highest disparity families. Yet, poor communities generally experience fractured systems of care, a paucity of providers, and limited resources to deliver intensive home-visiting models to families who stand to benefit most. This article explores lessons emerging from the recent Tribal Maternal and Infant Early Childhood Home Visiting (MIECHV) legislation supporting delivery of home-visiting interventions in low-income, hard-to-reach American Indian and Alaska Native communities. We draw experience from four diverse tribal communities that participated in the Tribal MIECHV Program and overcame socioeconomic, geographic, and structural challenges that called for both early childhood home-visiting services and increased the difficulty of delivery. Key innovations are described, including unique community engagement, recruitment and retention strategies, expanded case management roles of home visitors to overcome fragmented care systems, contextual demands for employing paraprofessional home visitors, and practical advances toward streamlined evaluation approaches. We draw on the concept of "frugal innovation" to explain how the experience of Tribal MIECHV participation has led to more efficient, effective, and culturally informed early childhood home-visiting service delivery, with lessons for future dissemination to underserved communities in the United States and abroad.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".