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Record W1534566115 · doi:10.1002/9781118413555.ch23

Sustaining knowledge use

2013· other· en· W1534566115 on OpenAlexaff
Barbara Davies, Nancy Edwards

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSustainabilityProcess managementProcess (computing)Knowledge managementBusinessHealth careAction (physics)Computer sciencePolitical science

Abstract

fetched live from OpenAlex

Sustained knowledge use refers to the continued implementation of innovations over time and depends on the ability of workers, organizations, and health care delivery systems to adapt to change. One well developed sustainability model with a diagnostic structured questionnaire and a weighted scoring system is available along with tools and guides for leaders from the NHS Institute for Innovation and Improvement in the UK. This sustainability model revolves around three core elements of process, staff, and organization. The authors in this chapter suggest six factors to be considered in the development of sustainability-oriented action plans. Sustainability, spread and scaling up need to be considered from a health care provider, inter-professional team, hospital/community, and health systems perspective. Monitoring systems and data feedback mechanisms are needed to determine relevant process and outcome factors to assess sustainability.

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)0.012
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.004
Scholarly communication0.0100.010
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0220.008

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 head0.499
GPT teacher head0.456
Teacher spread0.042 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations44
Published2013
Admission routes1
Has abstractyes

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