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Record W4385637916 · doi:10.1186/s12961-023-01028-z

The Implementation in Context (ICON) Framework: A meta-framework of context domains, attributes and features in healthcare

2023· article· en· W4385637916 on OpenAlexafffund
Janet E. Squires, Ian D. Graham, Wilmer J. Santos, Alison M. Hutchinson, Chantal Backman, Anna Bergström, Jamie Brehaut, Melissa Brouwers, Christopher R Burton, Ligyana Korki de Cândido, Christine Cassidy, Cheyne Chalmers, Anna Chapman, Heather Colquhoun, Janet Curran, Melissa Demery Varin, Paula Doering, Annette Elliott Rose, Lee Fairclough, Jill Francis, Christina Godfrey, Megan Greenough, Jeremy Grimshaw, Doris Grinspun, Gill Harvey, Michael Hillmer, Noah Ivers, John N. Lavis, Shelly‐Anne Li, Susan Michie, Wayne C. Miller, Thomas Noseworthy, Tamara Rader, Mark E. Robson, Jo Rycroft‐Malone, Dawn Stacey, Sharon E. Straus, Andrea C. Tricco, Lars Wallin, Vanessa Watkins

Bibliographic record

VenueHealth Research Policy and Systems · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchBarwon Health FoundationUppsala UniversitetKillam TrustsOntario Ministry of Health and Long-Term CareDalhousie UniversityUniversity of TorontoFlinders UniversityUniversity College LondonRegistered Nurses' Association of OntarioDeakin UniversityHögskolan DalarnaQueen's UniversityCanterbury Christ Church UniversityUniversity of OttawaMcMaster UniversityOttawa Hospital Research Institute
KeywordsContext (archaeology)Health administrationIconHealth services researchHealth careMeta-analysisHealth informaticsPublic healthMedicineComputer scienceNursingPolitical scienceGeographyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is growing evidence that context mediates the effects of implementation interventions intended to increase healthcare professionals' use of research evidence in clinical practice. However, conceptual clarity about what comprises context is elusive. The purpose of this study was to advance conceptual clarity on context by developing the Implementation in Context Framework, a meta-framework of the context domains, attributes and features that can facilitate or hinder healthcare professionals' use of research evidence and the effectiveness of implementation interventions in clinical practice. METHODS: We conducted a meta-synthesis of data from three interrelated studies: (1) a concept analysis of published literature on context (n = 70 studies), (2) a secondary analysis of healthcare professional interviews (n = 145) examining context across 11 unique studies and (3) a descriptive qualitative study comprised of interviews with heath system stakeholders (n = 39) in four countries to elicit their tacit knowledge on the attributes and features of context. A rigorous protocol was followed for the meta-synthesis, resulting in development of the Implementation in Context Framework. Following this meta-synthesis, the framework was further refined through feedback from experts in context and implementation science. RESULTS: In the Implementation in Context Framework, context is conceptualized in three levels: micro (individual), meso (organizational), and macro (external). The three levels are composed of six contextual domains: (1) actors (micro), (2) organizational climate and structures (meso), (3) organizational social behaviour (meso), (4) organizational response to change (meso), (5) organizational processes (meso) and (6) external influences (macro). These six domains contain 22 core attributes of context and 108 features that illustrate these attributes. CONCLUSIONS: The Implementation in Context Framework is the only meta-framework of context available to guide implementation efforts of healthcare professionals. It provides a comprehensive and critically needed understanding of the context domains, attributes and features relevant to healthcare professionals' use of research evidence in clinical practice. The Implementation in Context Framework can inform implementation intervention design and delivery to better interpret the effects of implementation interventions, and pragmatically guide implementation efforts that enhance evidence uptake and sustainability by healthcare professionals.

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)0.043
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.684
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0430.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.666
GPT teacher head0.694
Teacher spread0.029 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations35
Published2023
Admission routes2
Has abstractyes

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