What would help low-income families?: Results from a North American survey of 2-1-1 helpline professionals
Bibliographic record
Abstract
Almost half of young American children live in low-income families, many with unmet needs that negatively impact health and life outcomes. Understanding which needs, proactively addressed, would most improve their lives would allow maternal and child health practitioners and social service providers to generate collaborative solutions with the potential to affect health in childhood and throughout the life course. 2-1-1 referral helplines respond to over 16 million inquiries annually, including millions of low-income parents seeking resources. Because 2-1-1 staff members understand the availability of community resources, we conducted an online survey to determine which solutions staff believed held most potential to improve the lives of children in low-income families. Information and referral specialists, resource managers, and call center directors (N = 471) from 44 states, Puerto Rico, and Canada ranked the needs of 2-1-1 callers with children based on which needs, if addressed, would help families most. Childcare (32%), parenting (29%), and child health/health care (23%) were rated most important. Across all childcare dimensions (e.g. quality affordable care, special needs care), over half of the respondents rated community resources inadequate. Findings will help practitioners develop screeners for needs assessment, prioritize resource referrals, and advocate for community resource 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".