Can Sharing Improve Caring? A Call to Prioritize Shared Decision Making in Pediatric Pain Management
Bibliographic record
Abstract
Objective: Families and children are not consistently included in pediatric pain management decisions. Shared decision making (SDM) is a collaborative process where health care professionals (HCPs) and families exchange information about treatment options along with families’ preferences to make an optimal health decision. SDM is recommended and beneficial for children, parents, and HCPs alike; however, the process of SDM has not been routinely integrated into pediatric pain management, despite families’ desire to be involved in these decisions. In this commentary, we discuss the implications of the dearth of literature about SDM in pediatric pain on clinical practice and highlight the potential benefits of engaging in SDM to improve pain management. Method: This commentary will describe clinical approaches and considerations to implementing SDM in pediatric pain management, such as consideration of families’ preferences for SDM, the quality of the evidence, and decisional complexity. Results: This commentary will propose recommendations to further advance the understanding and utility of SDM in pediatric pain, such as identifying opportunities to engage in SDM as well as factors that support its implementation. Conclusions: SDM provides a rich and structured opportunity to engage children and families in their care, while also creating opportunities for HCPs to engage in evidence-based practice. As such, SDM should be recognized as a key priority when engaging in best practices for pediatric pain management. Implications for Impact Statement Shared decision making (SDM) facilitates the engagement of children and families in pain management decisions. SDM is not yet common practice in pediatric pain; however, it holds promise to improve pain outcomes for children by increasing application of evidence-based practices as well as satisfaction with care.
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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 | 0.085 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.021 | 0.034 |
| Insufficient payload (model declined to judge) | 0.008 | 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".