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Record W2922687990 · doi:10.12927/hcq.2019.25742

Experimenting with Governance: Alberta’s Strategic Clinical Networks

2019· article· en· W2922687990 on OpenAlexaffvenueabout
Deborah White, Navjot Kaur Virk, Meghan Jackson, Henry T. Stelfox, Tracy Wasylak, William A. Ghali

Bibliographic record

VenueHealthcare Quarterly · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsCorporate governanceGovernment (linguistics)Health careClinical governanceHealthcare deliveryIntervention (counseling)Quality (philosophy)BusinessQuality managementBest practiceHealth professionalsPublic relationsMedicineNursingPolitical scienceEconomic growthMarketingFinanceEconomics

Abstract

fetched live from OpenAlex

Alberta is undertaking a bold and somewhat risky step overhauling its health system governance to build higher performance in quality, safety and improved health outcomes for Albertans. On the heels of having re-established a single province-wide health authority (Alberta Health Services [AHS]), provincial health system decision makers have moved to establish province-wide Strategic Clinical Networks™ (SCNs). Sixteen SCNs have been implemented, and all are constituted as teams of healthcare professionals, researchers, government stakeholders, patients and families seeking to improve delivery of healthcare across the province. SCNs were developed in part as a strategy for strengthening clinical engagement to achieve a broad range of healthcare delivery benefits including improvement of clinical care processes and reduced variations in practice, better access to care and improved patient outcomes across the province. Here, we examine the rationale and potential of this governance intervention, while also considering some of the fundamental questions around their potential impact and the ultimate need for multidimensional assessment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.113
GPT teacher head0.468
Teacher spread0.355 · 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 designObservational
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

Citations5
Published2019
Admission routes3
Has abstractyes

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