Adaptation, Translation, and Validation of a Patient-Reported Experience Measure for Children and Young People for the Canadian Context
Bibliographic record
Abstract
BACKGROUND: Patient-reported experience measures (PREMs) evaluate children's and young people's (CYP) perceptions of care. An important PREM developed with and for children was created in London, UK. Given the absence of similar North American instruments, we aimed to adapt, translate, and linguistically validate this instrument for use in a Canadian pediatric outpatient setting. METHODS: A qualitative design was used, involving CYP and their parents/caregivers. Phase 1 entailed the English survey adaptation using think-aloud testing, revision, and cognitive testing. Phase 2 involved translation into French, revision and back-translation, and cognitive testing. Phase 3 encompassed a cross-validation of the English and French versions of the adapted instrument. RESULTS: Fifty-five children in 3 age groups (8-11y, 12-13y, 14-16y) participated in creating the Canadian PREM. In Phases 1 and 2, 41 children participated in reviewing and updating specific questions in the instrument, resulting in adjustments and revisions based on their feedback. In Phase 3, 14 bilingual children linguistically validated the PREM instrument. CONCLUSIONS: This study reports the development of the first Canadian PREM specifically tailored to children. By incorporating the perspectives and preferences of CYP in clinical practice, this approach has the potential to amplify the delivery of patient-centered care for this vulnerable population and ensure that the needs and voices of CYP are acknowledged. LEVEL OF EVIDENCE: V, Therapeutic.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".