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Record W2052495615 · doi:10.12927/hcq.2013.21670

Evaluation of the Consensus-Building Process to Develop a Balanced Scorecard for York Region Public Health

2010· article· en· W2052495615 on OpenAlexaff
Scott Cholewa, Kathy Moran, Yvonne Y. Cheung

Bibliographic record

VenueHealthcare Quarterly · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsRegional Municipality of Niagara
Fundersnot available
KeywordsBalanced scorecardBusinessHealth careProcess (computing)Performance measurementPublic healthBest practiceHealth administrationProcess managementPublic relationsPublic administrationOperations managementManagementMarketingMedicinePolitical scienceNursingEngineeringComputer scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

The Balanced Scorecard (BSC) was originally developed by Kaplan and Norton as a performance measurement tool for managers to obtain a quick, yet comprehensive view of how their businesses were operating (Kaplan and Norton 1992). Since 1992, the BSC has been adopted by a wide range of industries, including hospitals, universities, long-term care facilities, psychiatric centres, insurance companies, pharmaceutical companies, national healthcare organizations and federal and local governments

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)0.292
metaresearch head score (Gemma)0.326
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.873

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2920.326
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.006
Science and technology studies0.0030.002
Scholarly communication0.0040.005
Open science0.0030.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.633
GPT teacher head0.642
Teacher spread0.009 · 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.

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

Citations0
Published2010
Admission routes1
Has abstractyes

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