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Record W1920773305 · doi:10.1055/s-0038-1634039

Lest Formalisms Impede Insight and Success: Evaluation in Health Informatics

2006· article· en· W1920773305 on OpenAlexaff
C Anglin, Joseph Schaafsma, Stefan V. Pantazi, Nicole A Grimm, Jochen R. Moehr

Bibliographic record

VenueMethods of Information in Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTelehealthFormative assessmentInformaticsProcess managementInformation systemKnowledge managementComputer scienceManagement scienceHealth informaticsEngineering managementRisk analysis (engineering)MedicineHealth careBusinessPsychologyNursingEngineeringTelemedicinePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: To illustrate the advantages of an open-ended formative evaluation approach using a project-specific selection of methods over the controlled trial approach in the evaluation of health information systems. To illustrate factors leading to success and others impeding it in a telehealth project. METHODS: The methods and results of an evaluation of the BC Telehealth Program are summarized. RESULTS: The evaluation gave a comprehensive picture of the project, including assessment of the effects of an array of telehealth applications, and their economic impact. Factors leading to success and others preventing it are identified from the level of overall program management to the project specifics. The results include unanticipated effects and explanations for their reasons of occurrence. Neither the comprehensiveness of information nor the timeliness was achieved in a related project using a controlled trial approach. CONCLUSIONS: Not all types of health information system projects can be evaluated using the controlled trial approach. This approach may impede important insights. It is also usually much less efficient. Funding agencies and journal editors have to take this into account when selecting projects for funding and submissions for publication.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.054
GPT teacher head0.466
Teacher spread0.412 · 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 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

Citations6
Published2006
Admission routes1
Has abstractyes

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