Ethical and practical considerations related to data sharing when collecting patient-reported outcomes in care-based child health research
Bibliographic record
Abstract
PURPOSE: The collection and use of patient reported outcomes (PROs) in care-based child health research raises challenging ethical and logistical questions. This paper offers an analysis of two questions related to PROs in child health research: (1) Is it ethically obligatory, desirable or preferable to share PRO data collected for research with children, families, and health care providers? And if so, (2) What are the characteristics of a model best suited to guide the collection, monitoring, and sharing of these data? METHODS: A multidisciplinary team of researchers, providers, patient and family partners, and ethicists examined the literature and identified a need for focus on PRO sharing in pediatric care-based research. We constructed and analyzed three models for managing pediatric PRO data in care-based research, drawing on ethical principles, logistics, and opportunities to engage with children and families. RESULTS: We argue that it is preferable to share pediatric PRO data with providers, but to manage expectations and balance the risks and benefits of research, this requires a justifiable data sharing model. We argue that a successful PRO data sharing model will allow children and families to have access to and control over their own PRO data and be engaged in decision-making around how PROs collected for research may be integrated into care, but require support from providers. CONCLUSION: We propose a PRO data sharing model that can be used across diverse research settings and contributes to improved transparency, communication, and patient-centered care and research.
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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 | 0.778 | 0.748 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.015 | 0.055 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".