Facilitators and Barriers to Access to Pediatric Medical Services in a Community Hospital
Bibliographic record
Abstract
Background: Missed medical appointments decrease continuity of medical care, waste resources, and may affect health outcomes. We examined the factors associated with missed children’s supervision visits in Eastern Brooklyn, NY, USA. Methods: We surveyed guardians whose children received routine medical care at four pediatric clinics. Participants filled out a questionnaire that queried: demographics, food security, recent relocation, parental support of healthy behaviors, and length of knowing provider. Preexisting disease(s) and missed visits were retrieved from medical records. Regression analyses were used to determine factors that were associated with missing medical appointments. Results: Among 213 families, 33% faced food insecurity and 16.4% reported moving within the past 12 months. Forty percent of children missed at least 1 visit. Food insecurity (adjusted odds ratio [aOR] 2.3, 95% confidence interval [CI 1.0% to 5.2%) and recent relocation (aOR 1.8, 95% CI 1.1-3.4 were associated with missed health supervision visits, whereas greater parental healthy behaviors (aOR 0.5, 95% CI 0.3-0.9) and longer length of knowing provider (aOR 0.8, 95% CI 0.7-1.0) were associated with fewer missed appointments. Conclusion: This study indicates that social inequity may contribute to poor adherence to medical appointments through multiple mechanisms, including food insecurity, lack of social stability, and parental health behaviors. Multidimensional proactive prevention, and reactive tolerance should be considered as opportunities to mitigate the impact of social inequity on health outcomes.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".