Call to focus on digital health technologies in hospitalized children's pain care: clinician experts' qualitative insights on optimizing electronic medical records to improve care
Bibliographic record
Abstract
ABSTRACT: Most hospitalized children experience pain that is often inadequately assessed and undertreated. Exposure to undertreated childhood pain is associated with negative short-term and long-term outcomes and can detrimentally affect families, health services, and communities. Adopting electronic medical records (EMRs) in pediatric hospitals is a promising mechanism to transform care. As part of a larger program of research, this study examined the perspectives of pediatric clinical pain experts about how to capitalize on EMR designs to drive optimal family-centered pain care. A qualitative descriptive study design was used and 14 nursing and medical experts from 5 countries (United States, Canada, United Kingdom, Australia, and Qatar) were interviewed online using Zoom for Healthcare. We applied a reflexive content analysis to the data and constructed 4 broad categories: "capturing the pain story," "working with user-friendly systems," "patient and family engagement and shared decision making," and "augmenting pain knowledge and awareness." These findings outline expert recommendations for EMR designs that facilitate broad biopsychosocial pain assessments and multimodal treatments, and customized functionality that safeguards high-risk practices without overwhelming clinicians. Future research should study the use of patient-controlled and family-controlled interactive bedside technology to and their potential to promote shared decision making and optimize pain care outcomes.
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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 | 0.034 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".