Factors affecting implementation of patient-reported outcome and experience measures in a pediatric health system
Bibliographic record
Abstract
Abstract Background The use of patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) in pediatric clinical practice can enhance clinical care and bring children and families’ perspectives into evaluations of healthcare services. Implementing these measures is complex and requires a thorough assessment of the context of implementation The purpose of this study is to describe the barriers and facilitators to PROMs and PREMs implementation and to recommend strategies for implementing these measures in a pediatric health system. Methods We used a qualitative descriptive approach to analyse data from interviews to understand the experiences of PROMs and PREMs users across different pediatric settings in a single Canadian healthcare system. Results There were 23 participants representing a variety of roles within the healthcare system and pediatric populations. We found five main factors that affected implementation of PROMs and PREMs in pediatric settings: 1) Characteristics of PROMs and PREMs; 2) Individual’s beliefs; 3) Administering PROMs and PREMs; 4) Designing clinical workflows; and 5) Incentives for using PROMs and PREMs. Thirteen recommendations for integrating PROMs and PREMs in pediatric health settings are provided. Conclusions Implementing and sustaining the use of PROMs and PREMs in pediatric health settings presents several challenges. The information presented will be useful for individuals who are planning or evaluating the implementation of PROMs and PREMs in pediatric settings.
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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 | 0.022 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".