Assessing the Impact of a Telemedicine Program on Caregivers of Children With Complex Chronic Conditions: Pre-Post Intervention Study
Bibliographic record
Abstract
Background: The COVID-19 pandemic was a paradigm shift in global health care. This situation highlighted the role of telemedicine in adapting to the care requirements of pediatric patients and their families by facilitating remote consultations and ensuring continuity of care. Objective: We aimed to establish the usefulness of a telemedicine program for caregivers of children with complex chronic conditions. Methods: We performed a quasi-experimental pre-post intervention study of a telemedicine program, regarding health care system use and caregiver quality of life while comparing two periods: before and during the COVID-19 pandemic. This study included caregivers of children with complex chronic conditions followed-up in a specialized unit. Results: A total of 34 families were included. The mean number of visits per year was higher in the preintervention period for both primary care consultations (P=.03) and hospital-specialized medical consultations (P=.03). The number of emergency room visits per year was lower in the pandemic period compared to the prepandemic period (P=.01). In both groups, an improvement in caregiver quality of life was detected at 12 months (P=.03). However, the Rosenberg self-esteem scale score for the primary caregiver was significantly lower at 3 months compared to the baseline (P=.03). Conclusions: Our study demonstrates that the use of a telemedicine program resulted in decreased scheduled face-to-face care visits and a reduction in the number of emergency department visits. Regarding the caregiver quality of life, outcomes were poorer among families whose children were affected by neurological conditions or were diagnosed at an older age.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".