Evaluation of Shared Decision-Making Interventions in Pediatric Acute Care: A Systematic Review
Bibliographic record
Abstract
CONTEXT: There is limited consensus on the effectiveness of shared decision-making (SDM) interventions in pediatric acute care, where implementing SDM is particularly challenging. OBJECTIVE: To conduct a systematic review on the effectiveness of SDM interventions in pediatric acute care settings (PROSPERO: CRD42023394760). DATA SOURCES: MEDLINE, EMBASE, Cochrane Library, CINAHL, Web of Science, Scopus, and PsycInfo databases from inception to November 12, 2024. STUDY SELECTION: Studies that evaluated SDM intervention effectiveness for managing acute medical problems-those requiring an urgent or time-sensitive decision at the current clinical visit-in children and youth (aged <19 years). DATA EXTRACTION: Data were extracted on study participants, study design, clinical decision assessed, and patient-centered and clinical outcomes evaluated. RESULTS: Of 10 278 articles identified, 27 studies were included. These studies focused on acute respiratory infection (n = 5), intensive care unit decision (n = 5), head injury (n = 4), appendicitis (n = 4), febrile infant (n = 3), and other care decisions (n = 6). A breadth of outcome measures and measurement tools were used. In general, SDM interventions had positive impacts on patient-centered and clinical outcomes and were not accompanied by increased resource use, repeat health care utilization, or complications. LIMITATIONS: Heterogeneity in SDM interventions and outcome measures limited the ability to conduct meta-analyses on intervention effectiveness. CONCLUSIONS: SDM interventions have been evaluated in several pediatric acute care settings. Across a range of studies, SDM interventions were observed to improve patient-centered outcomes without increasing complications. Additional research using standardized outcome measurements is needed.
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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 | 0.036 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".