Experiences with Virtual Health Care for Children with Chronic Conditions Requiring High Needs for Pediatric Subspecialist Care and their Caregivers
Bibliographic record
Abstract
Purpose: This thesis investigated virtual health care experiences among children with high health care needs and their caregivers. Methods: Using a scoping review, we synthesized studies of virtual care experiences for children with medical complexity. Analyzing cross-sectional survey data from an existing Canadian study, we then examined caregiver-reported experiences with virtual care for children with inherited metabolic diseases (IMDs) during the COVID-19 pandemic. Results: Of 34 studies included in the scoping review, we identified inconsistencies in the conceptualization and measurement of virtual care, and limited attention to experiences specific to sociodemographic subgroups (5 studies). Seventy-one caregivers participated in the survey; they endorsed benefits of virtual care such as a reduced need to travel and challenges such as communication barriers. Conclusions: Findings highlight the need for harmonized, equity-focused frameworks for evaluating virtual care. While virtual care presented benefits, some challenges persisted, suggesting areas for improvement in post-pandemic virtual care for pediatric 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".