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Record W3103842672 · doi:10.3138/cjpe.68127

Comparing and Contrasting a Program versus System Approach to Evaluation: The Example of a Cardiac Care System

2020· article· en· W3103842672 on OpenAlexvenueno aff
Ralph Renger, Jessica Renger, Stewart I. Donaldson, Jirina Renger, Gary Hart, Andrew Hawkins

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Perspective (graphical)Set (abstract data type)Systems theoryManagement scienceComputer scienceSystems thinkingRisk analysis (engineering)MedicineArtificial intelligenceEngineeringProgramming language

Abstract

fetched live from OpenAlex

Abstract: This article examines the differences between approaching an evaluation problem from a program perspective and doing so from a systems perspective. The terms program, systems, systems thinking, and systems concepts are first defined. Then, using an actual evaluation of a cardiac care system, it is shown how initial investments in a program theory approach were deemed inadequate to account for the influence of external factors on patient outcomes. It was decided that a systems thinking approach was more appropriate for evaluating the interactions between several agencies comprising the cardiac care system. It is then shown how System Evaluation Theory (SET) was used to systematically apply different systems concepts to define and evaluate the cardiac care system. The discussion compares and contrasts the program and system evaluation approaches, noting the conditions under which each is more appropriate. It concludes by noting scope and cost differences between the two approaches.

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)0.016
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.002
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)0.0000.000

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 head0.592
GPT teacher head0.487
Teacher spread0.105 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2020
Admission routes1
Has abstractyes

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