MétaCan
Menu
Back to cohort
Record W4410524631 · doi:10.1080/01900692.2025.2495344

Operationalizing Decentralized Governance Mechanisms in Health Systems with Insights from Decentralized Digital Technologies

2025· article· en· W4410524631 on OpenAlex
Arianna Bhagwansingh

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Public Administration · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsOperationalizationCorporate governanceBusinessDigital healthProcess managementKnowledge managementPublic relationsPublic administrationComputer sciencePolitical scienceEconomicsHealth careEconomic growth

Abstract

fetched live from OpenAlex

The scholarly literature on decentralized health systems is inconclusive on whether they achieve greater equity, efficiency, and resiliency. A key problem is a lack of conceptual clarity surrounding the term decentralization. This paper traces the storyline on decentralization and outlines the inadequacy of standard frameworks to causally link decentralized governance practices with system outcomes. In so doing, mechanisms for operationalizing decentralization reforms are illuminated. A new analytical framework with tools from decentralized digital technologies is presented and applied to three Canadian provincial health system reforms, demonstrating how it provides practical insights into the relationship between decentralized governance and decentralized outcomes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0030.008
Open science0.0000.000
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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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