Patient Perceptions of Telehealth for Pediatric Type 1 Diabetes During the COVID-19 Pandemic: A Follow-up Study
Bibliographic record
Abstract
OBJECTIVES: There was rapid uptake of pediatric diabetes telehealth at the onset of the COVID-19 pandemic and initial studies demonstrated good usability and satisfaction. As exposure to telehealth continued to increase during the pandemic, we aimed to determine changes in telehealth usability and changes in future preferences for telehealth care. METHODS: A telehealth questionnaire was administered early in the pandemic and again more than 1 year later. Survey data were linked with a clinical data registry. A multivariable proportional odds logistic mixed-effects model was used to assess the association between exposure to telehealth and outcome of future preference for telehealth. Multivariable linear mixed-effects models were used to examine associations between exposure to early and later pandemic periods and the outcome of usability scores. RESULTS: Survey response rate was 40%, with 87 early and 168 later period participants. Virtual visits increased from 46% to 92% of all telehealth visits. Virtual visits improved in "ease of use" (p=0.0013) and "satisfaction" (p=0.045); there were no improvements in telephone visits. The odds of indicating higher preference for more future telehealth visits was 5.1-fold higher in the later pandemic group (p=0.0298). Eighty percent of participants would like their future care to include telehealth visits. CONCLUSIONS: At our tertiary diabetes centre, families' desire for future telehealth care has increased during this 1-year period of additional telehealth exposure, and virtual care has now become the preferred option. This study provides important family perspectives that can help guide development of future diabetes clinical care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".