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Record W2333509042 · doi:10.1332/174426413x662798

Understanding whole systems change in health care: insights into system level diffusion from nursing service delivery innovations – a multiple case study

2014· article· en· W2333509042 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEvidence & Policy · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of OttawaRegistered Nurses' Association of OntarioUniversity of Toronto
Fundersnot available
KeywordsInterdependenceSituatedWork (physics)Health care deliveryDiffusionHealth careDiffusion of innovationsService delivery frameworkComplex adaptive systemProcess managementHealthcare systemBusinessKnowledge managementService (business)Computer scienceRisk analysis (engineering)SociologyMarketingEngineeringEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Our study responds to calls for theory-driven approaches to studying innovation diffusion processes in health care. While most research on diffusion in health care is situated at the service delivery level, we study innovations and associated processes that have diffused to the system level, and refer to work on complex adaptive systems and whole systems change to guide our work. Systemlevel diffusion not only involves the spread of innovations across sector boundaries in a system, it may alter interactions and care delivery within multiple system components, change the nature of the interdependencies between components, and ultimately lead to whole systems change.

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.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.552
GPT teacher head0.471
Teacher spread0.081 · 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