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Record W2098906815 · doi:10.5334/ijic.576

Use of a knowledge synthesis by decision makers and planners to facilitate system level integration in a large Canadian provincial health authority

2011· article· en· W2098906815 on OpenAlexaffabout
Esther Suter, Gail Armitage

Bibliographic record

VenueInternational Journal of Integrated Care · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsAlberta HealthAlberta Health Services
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

PURPOSE: The study is an examination of how a knowledge synthesis, conducted to fill an information gap identified by decision makers and planners responsible for integrating health systems in a western Canadian health authority, is being used within that organization. METHODS: Purposive sampling and snowball technique were used to identify 13 participants who were interviewed about how they are using the knowledge synthesis for health services planning and decision-making. RESULTS: The knowledge synthesis is used by those involved in the strategic direction of the provincial healthcare organization and those tasked with the operationalization of integration at the provincial or local level. Both groups most frequently use the 10 key principles for integration, followed by the sections on integration processes, strategies and models. The key principles facilitate discussion on priority areas to be considered and provide a reference point for a desired future state. Perceived information gaps relate to a lack of detail on 'how to' strategies, tools and processes that would lead to successful integration. DISCUSSION AND CONCLUSION: The current project demonstrates that decision makers and planners will effectively use a knowledge synthesis if it is timely, relevant and accessible. The information can be applied at strategic and operations levels. Attention needs to be paid to include more information on implementation strategies and processes. Including knowledge users in identifying research questions will increase information uptake.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.489
GPT teacher head0.538
Teacher spread0.049 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2011
Admission routes2
Has abstractyes

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