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Record W4379012364 · doi:10.21203/rs.3.rs-2333454/v1

A systematic review to determine the effect of strategies to sustain chronic disease prevention interventions in clinical and community settings: study protocol

2023· review· en· W4379012364 on OpenAlexaff
Edward Riley-Gibson, Alix Hall, Adam Shoesmith, Luke Wolfenden, Rachel C. Shelton, Emma Doherty, Emma Pollock, Debbie Booth, Ramzi G. Salloum, Celia Laur, Byron J. Powell, Melanie Kingsland, Cassandra Lane, Maji Hailemariam, Rachel Sutherland, Nicole Nathan

Bibliographic record

VenueResearch Square · 2023
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of Toronto
FundersNational Health and Medical Research CouncilMedical Research CouncilAgency for Healthcare Research and QualityNational Institutes of HealthClinical and Translational Science Institute, University of FloridaNational Center for Advancing Translational SciencesUniversity of Florida
KeywordsChecklistPsychological interventionSystematic reviewProtocol (science)Data extractionMedicineGrey literatureMEDLINEHealth careAlternative medicinePsychologyNursingPathology

Abstract

fetched live from OpenAlex

Background: The primary purpose of this review is to synthesise the effect of strategies aiming to sustain the implementation of evidenced based interventions (EBIs) targeting key health behaviours associated with chronic disease (i.e., physical inactivity, poor diet, harmful alcohol use and tobacco smoking) in clinical and community settings. The field of implementation science is bereft of an evidence base of effective sustainment strategies, and as such this review will provide important evidence to advance the field of sustainability research. Methods: This systematic review protocol is reported in accordance with the Preferred Reporting Items for Systematic review and Meta-Analysis Protocol (PRISMA-P) checklist (Additional file 1). Methods will follow Cochrane gold-standard review methodology. The search will be undertaken across multiple databases, adapting filters previously developed by the research team; data screening and extraction will be performed in duplicate; strategies will be coded using an adapted sustainability-explicit taxonomy; evidence will be synthesised using appropriate methods (i.e. meta-analytic following Cochrane or non-meta-analytic following SWiM guidelines). We will include any randomised controlled study that targets any staff or volunteers delivering interventions in clinical or community settings. Studies which report on any objective or subjective measure of the sustainment of a health prevention policy, practice, or program within any of the eligible settings will be included. Article screening, data extraction, risk of bias and quality assessment will be performed independently by two review authors. Risk of bias will be assessed using Version 2 of the Cochrane risk-of-bias tool for randomised trials (RoB 2). A random effect meta-analysis will be conducted to estimate the pooled effect of sustainment strategies separately by setting (i.e. clinical and community). Sub-group analyses will be undertaken to explore possible causes of statistical heterogeneity and may include: time period, single or multi strategy, type of setting and type of intervention. Differences between sub-groups will be statistically compared. Discussion/Conclusion: This will be the first systematic review to determine the effect of strategies designed to support sustainment on sustaining the implementation of EBIs in clinical and community settings. The findings of this review will directly inform the design of future sustainability-focused implementation trials. Further, these findings will inform the development of a sustainability practice guide for public health practitioners. Registration: This review was prospectively registered with PROSPERO (registration ID: CRD42022352333).

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)0.108
metaresearch head score (Gemma)0.152
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.108
Threshold uncertainty score0.571

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1080.152
Meta-epidemiology (narrow)0.0070.007
Meta-epidemiology (broad)0.0210.019
Bibliometrics0.0140.017
Science and technology studies0.0050.005
Scholarly communication0.0080.012
Open science0.0060.007
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0920.015

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 head0.825
GPT teacher head0.818
Teacher spread0.007 · 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 designSystematic review
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

Citations0
Published2023
Admission routes1
Has abstractyes

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