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Record W4393193979 · doi:10.1186/s13643-024-02512-5

Barriers and enablers to the implementation of patient-reported outcome and experience measures (PROMs/PREMs): protocol for an umbrella review

2024· article· en· W4393193979 on OpenAlexafffund
Guillaume Fontaine, Marie-Ève Poitras, Maxime Sasseville, Marie‐Pascale Pomey, Jérôme Ouellet, Lydia Ould Brahim, Sydney Wasserman, Frédéric Bergeron, Sylvie Lambert

Bibliographic record

VenueSystematic Reviews · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de MontréalUniversité LavalInstitut Universitaire en Santé Mentale de QuébecCentre Hospitalier de l’Université de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanMcGill UniversityJewish General HospitalUniversité de SherbrookeMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéRéseau de recherche portant sur les interventions en sciences infirmières du Québec
KeywordsSystematic reviewMedicineCritical appraisalPatient-reported outcomeChecklistHealth careProtocol (science)Best practiceGrey literatureGrading (engineering)MEDLINEQualitative researchManagement scienceNursingQuality of life (healthcare)Alternative medicinePsychologyManagement

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.141
metaresearch head score (Gemma)0.161
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.141
Threshold uncertainty score0.744

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1410.161
Meta-epidemiology (narrow)0.0070.007
Meta-epidemiology (broad)0.0120.017
Bibliometrics0.0170.016
Science and technology studies0.0060.006
Scholarly communication0.0090.009
Open science0.0070.010
Research integrity0.0110.009
Insufficient payload (model declined to judge)0.0770.021

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.

Opus teacher head0.496
GPT teacher head0.569
Teacher spread0.073 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations18
Published2024
Admission routes2
Has abstractyes

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