Use of Patient-Reported Experience Measures in Pediatric Care: A Systematic Review
Bibliographic record
Abstract
Introduction: Patient-reported Experience Measures (PREMs) are validated questionnaires, that gather patients' and families' views of their experience receiving care and are commonly used to measure the quality of care, with the goal to make care more patient and family-centered. PREMs are increasingly being adopted in pediatric population, however knowledge gaps exist around understanding the use of PREMs in pediatrics. Objective: To identify and synthesize evidence on the use of PREMs in pediatric healthcare settings and their characteristics. Evidence Review: Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines governed the conduct and reporting of this review. An exhaustive search strategy was applied to MEDLINE, EMBASE, PsycINFO, Cochrane Library, and CINAHL databases to identify relevant peer-reviewed articles from high-income countries. Additionally, gray literature was searched to capture real-world implementation of PREMs. All the articles were screened independently by two reviewers in two steps. Data was extracted independently, synthesized, and tabulated. Findings from gray literature was synthesized and reported separately. Risk of bias for the studies identified through scientific databases was assessed independently by two reviewers using the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: The initial search identified 15,457 articles. After removing duplicates, the title and abstracts of 11,543 articles were screened. Seven hundred ten articles were eligible for full-text review. Finally, 83 articles met the criteria and were included in the analyses. Of the 83 includes studies conducted in 14 countries, 48 were conducted in USA, 25 in European countries and 10 in other countries. These 83 studies reported on the use of 39 different PREMs in pediatric healthcare settings. The gray literature retrieved 10 additional PREMs. The number of items in these PREMs ranged from 7 to 89. Twenty-three PREMs were designed to be completed by proxy, 10 by either pediatric patients or family caregivers, and 6 by pediatric patients themselves. Conclusion and Relevance: This comprehensive review is the first to systematically search evidence around the use of PREMs in pediatrics. The findings of this review can guide health administrators and researchers to use appropriate PREMs to implement patient and family-centered care in pediatrics.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".