Impact of COVID-19 Pandemic on Pediatric Infectious Disease Telehealth Practices in North America
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has driven a significant increase in the use of telehealth (TH) but little is published about changes in TH usage by pediatric infectious disease (PID) providers. We assessed their pre- and intra-pandemic TH usage and experience. METHODS: The Pediatric Infectious Diseases Society Telehealth Work Group surveyed PID specialists in the United States and Canada from 6 December 2020 until 26 February 2021. Data collected included TH modalities, barriers, and satisfaction. RESULTS: The survey response rate was 11.3% (288 of 2,550 PID clinicians) with 243 (96% of 253 analysis-eligible responses) managing children only. Women accounted for 62.1% (n = 157), 51.4% (130) of respondents devoted 50-99% of their time to direct patient care, and 93.3% (236) were located in the United States. The greatest increase in TH usage during the pandemic was in synchronous provider-patient communications (3.9-fold increase). During the pandemic, provider-provider TH increased by less than 10%, comfort with TH usage doubled from 42% to 91%, and satisfaction grew from 74% to 93.3% with different aspects of TH. The top challenge was incomplete or no physical examination (182, 71.9%). Multivariate analysis showed that pre-pandemic TH usage and lack of barriers, but not reimbursement, were significantly associated with higher intra-pandemic usage. EMR-integrated TH was associated with significantly higher usage and satisfaction. Over 70% of respondents anticipate continuing TH usage after the pandemic. CONCLUSIONS: There was high intra-pandemic usage of, and increased comfort and satisfaction with telehealth by PID specialists. Our data help inform post-pandemic TH expectations and strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".