Parent-Reported Use of Pediatric Primary Care Telemedicine: Survey Study
Bibliographic record
Abstract
BACKGROUND: Telemedicine delivered from primary care practices became widely available for children during the COVID-19 pandemic. OBJECTIVE: Focusing on children with a usual source of care, we aimed to examine factors associated with use of primary care telemedicine. METHODS: In February 2022, we surveyed parents of children aged ≤17 years on the AmeriSpeak panel, a probability-based panel of representative US households, about their children's telemedicine use. We first compared sociodemographic factors among respondents who did and did not report a usual source of care for their children. Among those reporting a usual source of care, we used Rao-Scott F tests to examine factors associated with parent-reported use versus nonuse of primary care telemedicine for their children. RESULTS: Of 1206 respondents, 1054 reported a usual source of care for their children. Of these respondents, 301 of 1054 (weighted percentage 28%) reported primary care telemedicine visits for their children. Factors associated with primary care telemedicine use versus nonuse included having a child with a chronic medical condition (87/301, weighted percentage 27% vs 113/753, 15%, respectively; P=.002), metropolitan residence (262/301, weighted percentage 88% vs 598/753, 78%, respectively; P=.004), greater internet connectivity concerns (60/301, weighted percentage 24% vs 116/753, 16%, respectively; P=.05), and greater health literacy (285/301, weighted percentage 96% vs 693/753, 91%, respectively; P=.005). CONCLUSIONS: In a national sample of respondents with a usual source of care for their children, approximately one-quarter reported use of primary care telemedicine for their children as of 2022. Equitable access to primary care telemedicine may be enhanced by promoting access to primary care, sustaining payment for primary care telemedicine, addressing barriers in nonmetropolitan practices, and designing for lower health-literacy populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".