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Record W2985992387 · doi:10.1108/jhom-05-2019-0139

Evaluating complex transformation

2019· article· en· W2985992387 on OpenAlexaff
Allan Best, Narelle Ong, Penny Cooper, Carolyn Davison, Katherine Coatta, Alex Berland, Carol P. Herbert, Craig Mitton, John S. Millar, Stephen Reichert, Allison Cano

Bibliographic record

VenueJournal of Health Organization and Management · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsGovernment of British ColumbiaNutrasourceGolder Associates (Canada)University of British Columbia
Fundersnot available
KeywordsScope (computer science)Summative assessmentOriginalityProcess managementGovernment (linguistics)Scale (ratio)Theory of changeMedicineManagement scienceComputer scienceSociologyBusinessFormative assessmentEngineeringQualitative research

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this paper is to present a detailed case study of the evaluation strategies of a complex, multi-faceted response to a public health emergency: drug-related overdose deaths. It sets out the challenges of evaluating such a complex response and how they were overcome. It provides a pragmatic example of the rationale and issues faced to address the what, the why and particularly the how of the evaluation. DESIGN/METHODOLOGY/APPROACH: The case study overviews British Columbia's Provincial Response to the Overdose Public Health Emergency, and the aims and scope of its evaluation. It then outlines the conceptual approach taken to the evaluation, setting out key methodological challenges in evaluating large-scale, multi-level, multisectoral change. FINDINGS: The evaluation is developmental and summative, utilization focused and system informed. Defining the scope of the evaluation required a strong level of engagement with government leads, grantees and other evaluation stakeholders. Mixed method evaluation will be used to capture the complex pattern of relationships that have informed the overdose response. Working alongside people with drug use experience to both plan and inform the evaluation is critical to its success. ORIGINALITY/VALUE: This case study builds on a growing literature on evaluating large-scale and complex service transformation, providing a practical example of this.

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)0.007
metaresearch head score (Gemma)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.301
GPT teacher head0.548
Teacher spread0.247 · 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.

Study designOther design
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

Citations3
Published2019
Admission routes1
Has abstractyes

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