The Impact of COVID-19 on Pediatric Adherence and Self-Management
Bibliographic record
Abstract
The COVID-19 pandemic has presented unique circumstances that have the potential to both positively and negatively affect pediatric adherence and self-management in youth with chronic medical conditions. The following paper discusses how these circumstances (e.g., stay-at-home orders, school closures, changes in pediatric healthcare delivery) impact disease management at the individual, family, community, and healthcare system levels. We also discuss how barriers to pediatric adherence and self-management exacerbated by the pandemic may disproportionately affect underserved and vulnerable populations, potentially resulting in greater health disparities. Given the potential for widespread challenges to pediatric disease management during the pandemic, ongoing monitoring and promotion of adherence and self-management is critical. Technology offers several opportunities for this via telemedicine, electronic monitoring, and mobile apps. Moreover, pediatric psychologists are uniquely equipped to develop and implement adherence-promotion efforts to support youth and their families in achieving and sustaining optimal disease management as the current public health situation continues to evolve. Research efforts addressing the short- and long-term impact of the pandemic on pediatric adherence and self-management are needed to identify both risk and resilience factors affecting disease management and subsequent health outcomes during this unprecedented time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".