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Record W4214879934 · doi:10.12688/hrbopenres.13507.1

Characterising processes and outcomes of tailoring implementation strategies in healthcare: a protocol for a scoping review

2022· review· en· W4214879934 on OpenAlexaff
Fiona Riordan, Geoffrey M. Curran, Cara C. Lewis, Byron J. Powell, Justin Presseau, Luke Wolfenden, Sheena McHugh

Bibliographic record

VenueHRB Open Research · 2022
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOttawa Hospital
FundersHealth Research Board
KeywordsData extractionHealth careProcess (computing)Grey literatureMEDLINEPolitical scienceComputer science

Abstract

fetched live from OpenAlex

<ns4:p> <ns4:bold>Background:</ns4:bold> Tailoring strategies to target the salient barriers to and enablers of implementation is considered a critical step in supporting successful delivery of evidence based interventions in healthcare. <ns4:bold/> Theory, evidence, and stakeholder engagement are considered key ingredients in the process <ns4:bold/> however, these ingredients can be combined in different ways. There is no consensus on the definition of tailoring or single method for tailoring strategies to optimize impact, ensure transparency, and facilitate replication. </ns4:p> <ns4:p> <ns4:bold>Aim:</ns4:bold> The purpose of this scoping review is to <ns4:bold/> describe how tailoring has been undertaken within healthcare to answer questions about how it has been conceptualised, described, and conducted in practice, and to identify research gaps. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> The review will be conducted in accordance with best practice guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for scoping reviews (PRISMA-ScR) will be used to guide the reporting. Searches will be conducted of MEDLINE, Embase, Web of Science, Scopus, from 2005 to present. Reference lists of included articles will be searched. Grey literature will be searched on Google Scholar. Screening and data extraction will be conducted by two or more members of the research team, with any discrepancies resolved by consensus discussion with a third reviewer. Initial analysis will be quantitative involving a descriptive numerical summary of the characteristics of the studies and the tailoring process. Qualitative content analysis aligned to the research questions will also be conducted, and data managed using NVivo where applicable. This scoping review is pre-registered with the Open Science Framework. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> The findings will serve a resource for implementation researchers and practitioners to guide future research in this field and facilitate systematic, transparent, and replicable development of tailored implementation strategies. </ns4:p>

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)0.027
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.670
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.953
GPT teacher head0.842
Teacher spread0.112 · 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
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

Citations2
Published2022
Admission routes1
Has abstractyes

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