THE AMERICAN ACADEMY OF PEDIATRICS I-CATCH PROGRAM: IMPROVING CHILDREN'S ACCESS TO COMMUNITY-BASED CARE IN RESOURCE-LIMITED SETTINGS
Bibliographic record
Abstract
INTRODUCTION: The great disparities in children's access to health care depend on many factors. Expanding the availability of community-based services through programs designed to decrease these disparities is imperative. METHODS: In 2006, the American Academy of Pediatrics Section on International Child Health implemented a new program to address these disparities, the International Community Access to Child Health (I-CATCH) program, which offers mentorship in grant preparation and project execution and provides 3-year funding to support project development and implementation. Projects are community-based initiatives that increase children's access to health care or services not otherwise available. Project initiatives will decrease health disparities and will develop sustainable community-based child health programs that may be replicated in other communities. RESULTS: During the first grant cycle, innovative proposals were received from colleagues in 16 countries. A great variety of opportunities were described to improve children's access to health. Four projects were funded, each of which focused on community education and development: (1) improve children's nutrition and decrease gastrointestinal and respiratory disease (El Salvador); (2) train community health care workers (Pakistan); (3) identify and serve high-risk pregnancies and neonates (Philippines); and (4) promote essential newborn care (Uganda). CONCLUSIONS: The first grant cycle illuminated the impressive creativity of colleagues, who outlined many opportunities to improve children's access to care through community-based programs with the expectation of decreasing health disparities. The tremendous potential of the I-CATCH program was validated. Although assessment of the long-term impact of the I-CATCH program is needed, the initial year showed great promise.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".