“A very different place from when the pandemic started”: Lessons learned for improving systems of care for families of children with medical complexity
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) created unprecedented challenges for everyone, but especially families of children with medical complexity (MC) who rely on a comprehensive range of health and social services in their daily lives. Yet despite this, there are limited studies exploring caregiver perspectives regarding access to health and social services during the pandemic. To address this gap, we aimed to explore how health and social services can better meet the needs of children with MC and their families. Sixteen parents residing with their children with MC (from birth to 18 years) in British Columbia, Canada participated in semi-structured interviews between July 2021 and April 2022. Findings revealed two different areas to improve services for families of children with MC, those relating to technology and family-centered care. Parents prioritized expanding the use of digital communication tools to support service navigation and scheduling. Virtual platforms were viewed as being valuable for building connections with other families and their community. In terms of family-centered care, parents emphasized the importance of policies recognizing the physical, emotional, and financial needs of the family. Findings have important implications for improving services to enhance the well-being and quality of life of children with MC and their families.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".