Patient experience or patient satisfaction? A systematic review of child- and family-reported experience measures in pediatric surgery
Bibliographic record
Abstract
PURPOSE: Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are increasingly recognized as important health care quality indicators. PREMs measure patients' perception of the care they have received, differing from satisfaction ratings, which measure their expectations. The use of PREMs in pediatric surgery is limited, prompting this systematic review to assess their characteristics and identify areas for improvement. METHODS: A search was conducted in eight databases from inception until January 12, 2022, to identify PREMs used with pediatric surgical patients, with no language restrictions. We focused on studies of patient experience but also included studies that assessed satisfaction and sampled experience domains. The quality of the included studies was appraised using the Mixed Methods Appraisal Tool. RESULTS: Following title and abstract screening of 2633 studies, 51 were included for full-text review, of which 22 were subsequently excluded because they measured only patient satisfaction rather than experience, and 14 were excluded for a range of other reasons. Out of the 15 included studies, questionnaires used in 12 studies were proxy-reported by parents and in 3 by both parents and children; none focused only on the child. Most instruments were developed in-house for each specific study, without patients' involvement in the process, and were not validated. CONCLUSIONS: Although PROMs are increasingly used in pediatric surgery, PREMs are not yet in use, being typically substituted by satisfaction surveys. Significant efforts are needed to develop and implement PREMs in pediatric surgical care, in order to effectively capture children's and families' voices. LEVEL OF EVIDENCE: IV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 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) | 0.004 | 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".