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Mobilising knowledge in complex health systems: a call to action

2016· article· en· W2526711888 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 · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsNutrasourceMichael Smith Health Research BC
FundersMedical Research Council
KeywordsAction (physics)Key (lock)Call to actionKnowledge managementComplexity scienceWork (physics)Control (management)Computer scienceComplex systemHealthcare systemPublic relationsManagement scienceProcess managementPolitical scienceBusinessHealth careEngineeringArtificial intelligenceComputer securityMarketing

Abstract

fetched live from OpenAlex

Worldwide, policymakers, health system managers, practitioners and researchers struggle to use evidence to improve policy and practice. There is growing recognition that this challenge relates to the complex systems in which we work. The corresponding increase in complexity-related discourse remains primarily at a theoretical level. This paper moves the discussion to a practical level, proposing actions that can be taken to implement evidence successfully in complex systems. Key to success is working with, rather than trying to simplify or control, complexity. The integrated actions relate to co-producing knowledge, establishing shared goals and measures, enabling leadership, ensuring adequate resourcing, contributing to the science of knowledge-to-action, and communicating strategically.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.858
GPT teacher head0.757
Teacher spread0.101 · 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