Barriers and enablers to the implementation of patient-reported outcome and experience measures (PROMs/PREMs): protocol for an umbrella review
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome and experience measures (PROMs and PREMs, respectively) are evidence-based, standardized questionnaires that can be used to capture patients' perspectives of their health and health care. While substantial investments have been made in the implementation of PROMs and PREMs, their use remains fragmented and limited in many settings. Analysis of multi-level barriers and enablers to the implementation of PROMs and PREMs has been hampered by the lack of use of state-of-the-art implementation science frameworks. This umbrella review aims to consolidate available evidence from existing quantitative, qualitative, and mixed-methods systematic and scoping reviews covering factors that influence the implementation of PROMs and PREMs in healthcare settings. METHODS: An umbrella review of systematic and scoping reviews will be conducted following the guidelines of the Joanna Briggs Institute (JBI). Qualitative, quantitative, and mixed methods reviews of studies focusing on the implementation of PROMs and/or PREMs in all healthcare settings will be considered for inclusion. Eight bibliographical databases will be searched. All review steps will be conducted by two reviewers independently. Included reviews will be appraised and data will be extracted in four steps: (1) assessing the methodological quality of reviews using the JBI Critical Appraisal Checklist; (2) extracting data from included reviews; (3) theory-based coding of barriers and enablers using the Consolidated Framework for Implementation Research (CFIR) 2.0; and (4) identifying the barriers and enablers best supported by reviews using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual) approach. Findings will be presented in diagrammatic and tabular forms in a manner that aligns with the objective and scope of this umbrella review, along with a narrative summary. DISCUSSION: This umbrella review of quantitative, qualitative, and mixed-methods systematic and scoping reviews will inform policymakers, researchers, managers, and clinicians regarding which factors hamper or enable the adoption and sustained use of PROMs and PREMs in healthcare settings, and the level of confidence in the evidence supporting these factors. Findings will orient the selection and adaptation of implementation strategies tailored to the factors identified. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023421845.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".