Sociodemographic factors associated with access to preventative care for children
Bibliographic record
Abstract
Since the beginning of the 21st century, there has been an ongoing decline in children’s physicals, delayed immunizations, and well visits. This study investigates sociodemographic factors including poverty levels (PDL), employment status, adequacy of insurance, and race on the accessibility of preventative care check ups. The research from this study has been derived from the 2022 National Survey of Children's Health survey that sampled 53,621 participants from diverse socioeconomic and racial backgrounds. This study uses Binary regression analysis and Pearson's chi-squared tests to examine and test the association between key factors and access of care. For example, participants in the lowest poverty level (0-99%) experienced poorer health outcomes (27.2%) compared to those in 400%+ income level (12.4%). Furthermore, when compared to the unemployed or unpaid class of participants, the participants who work full-time are associated with better health outcomes (OR=1.219, 95% CI = 1.100, 1.352). Racial minorities, including Hispanic (OR=0.763), Black (OR=0.801), and Asian (OR=0.553) participants, showed lower odds of positive outcomes compared to participants of White descent. This research seeks to encourage the creation of targeted interventions strategies focusing on poverty reduction, stable income, and equitable, universal access to preventative measures for children across diverse populations. Keywords: preventative care, children, sociodemographic factors, United States
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".