Delays in Children’s Preventive Health Services During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Stay-at-home orders, social isolation recommendations, and fear of COVID-19 exposure have led to delays in children's preventive health services during the pandemic. Delays can lead to missed opportunities for early screening and detection of health problems, and increased risks for outbreaks of vaccine-preventable diseases. Understanding prevalence of and reasons for missed, delayed, or skipped preventive health services is important for developing strategies to achieve rapid catch-up of essential health services. METHODS: Using the Household Pulse Survey (n=37,064), a large, nationally-representative household survey fielded from April 14 to May 10, 2021, we examined prevalence of households with children who have missed, delayed, or skipped preventive health services, and factors associated with and reasons contributing to missed, delayed, or skipped preventive health services. RESULTS: About one-quarter of parents had children who missed, delayed, or skipped preventive check-ups in the past year. Delays in children's preventive health services were more common among respondents with higher education, households with greater numbers of children, and children who learned remotely or did not participate in formal education. Main reasons attributed to delayed preventive health services were limited appointments at health providers' offices (42.9%), concern about COVID-19 exposure at health providers' offices (42.2%), and closed health providers' offices due to the pandemic (29.0%). CONCLUSIONS: Physician office closures and concern about COVID-19 exposure resulted in over one-quarter of parents delaying preventive services for their children since the pandemic began. Coordinated efforts are needed to achieve rapid catch-up of preventive services and routine vaccines.
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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 | 0.009 |
| 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) | 0.004 | 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".