Is sharing really caring? Viewpoints on shared decision‐making in paediatrics
Bibliographic record
Abstract
Shared decision-making (SDM), the cornerstone of family-centred care and the gold standard in health decision-making, occurs when the patient, family members and the health-care team members partner to make health decisions about the child. This partnership involves an exchange of medical information and information about patient/family preferences and values. Together, the health-care team, parent and patient deliberate to determine the best course of action for the child. Despite high-quality evidence supporting its positive impact on outcomes, SDM has not been widely adopted in paediatric clinical practice. Greater understanding of the impact of SDM on all members of the decision triad (parent, patient and health-care provider) may increase the likelihood of SDM adoption. Therefore, we present the viewpoints of a paediatric patient, parent and paediatrician about the use of SDM. A youth living with a rare chronic disease discusses the impacts of being involved and excluded from health decisions. A mother of a son living with a rare nephrotic condition discusses working with a health-care team who are committed and skilled in SDM and the positive impacts SDM has had for her son's care. A general paediatrician with research expertise in SDM discusses the individual and system level challenges and rewards of using SDM in her clinical practice. Based on the viewpoints presented, we offer pragmatic recommendations for using SDM in paediatric clinical practice.
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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 | 0.086 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.053 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.015 | 0.033 |
| 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".