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Record W4399356350 · doi:10.1111/hex.14086

Strategies for involving patients and the public in scaling initiatives in health and social services: A scoping review

2024· review· en· W4399356350 on OpenAlexafffund
Roberta de Carvalho Corôa, Ali Ben Charif, Vincent Robitaille, Diogo Mochcovitch, M. Samri, Talagbé Gabin Akpo, Amédé Gogovor, Virginie Blanchette, Lucas Gomes Souza, Kathy Kastner, Amélie M. Achim, Robert K. D. McLean, Andrew Milat, France Légaré

Bibliographic record

VenueHealth Expectations · 2024
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsInternational Development Research CentreFirst Nations Health and Social Secretariat of ManitobaUniversité du Québec à Trois-RivièresCentres Intégré Universitaires de Santé et de Services SociauxInstitut National de la Recherche ScientifiqueCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleUniversité Laval
FundersNational Health and Medical Research CouncilCanadian Institutes of Health Research
KeywordsPsychological interventionIncentiveMEDLINESystematic reviewGrey literaturePublic healthFamily medicineMedicineHealth carePsychologyMedical educationPolitical scienceNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Scaling in health and social services (HSS) aims to increase the intended impact of proven effective interventions. Patient and public involvement (PPI) is critical for ensuring that scaling beneficiaries' interests are served. We aimed to identify PPI strategies and their characteristics in the science and practice of scaling in HSS. METHODS: In this scoping review, we included any scaling initiative in HSS that used PPI strategies and reported PPI methods and outcomes. We searched electronic databases (e.g., Medline) from inception to 5 February 2024, and grey literature (e.g., Google). Paired reviewers independently selected and extracted eligible reports. A narrative synthesis was performed and we used the PRISMA for Scoping Reviews and the Guidance for Reporting Involvement of Patients and the Public (GRIPP2). FINDINGS: We included 110 unique reports out of 24,579 records. In the past 5 years, the evidence on PPI in scaling has increased faster than in any previous period. We found 236 mutually nonexclusive PPI strategies among 120 scaling initiatives. Twenty-four initiatives did not target a specific country; but most of those that did so (n = 96) occurred in higher-income countries (n = 51). Community-based primary health care was the most frequent level of care (n = 103). Mostly, patients and the public were involved throughout all scaling phases (n = 46) and throughout the continuum of collaboration (n = 45); the most frequently reported ethical lens regarding the rationale for PPI was consequentialist-utilitarian (n = 96). Few papers reported PPI recruitment processes (n = 31) or incentives used (n = 18). PPI strategies occurred mostly in direct care (n = 88). Patient and public education was the PPI strategy most reported (n = 31), followed by population consultations (n = 30). CONCLUSIONS: PPI in scaling is increasing in HSS. Further investigation is needed to better document the PPI experience in scaling and ensure that it occurs in a meaningful and equitable way. PATIENT AND PUBLIC CONTRIBUTION: Two patients were involved in this review. They shared decisions on review questions, data collection instruments, protocol design, and findings dissemination. REVIEW REGISTRATION: Open Science Framework on 19 August 2020 (https://osf.io/zqpx7/).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.409
GPT teacher head0.554
Teacher spread0.145 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2024
Admission routes2
Has abstractyes

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