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Record W179393117 · doi:10.1055/s-0038-1634076

Where to in the Next Ten Years of Health Informatics Education?

2006· article· en· W179393117 on OpenAlexaff
Jochen R. Moehr

Bibliographic record

VenueMethods of Information in Medicine · 2006
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHealth informaticsInformaticsHealth Administration InformaticsHealth careMedical educationMedicineVocational educationPublic health informaticsData scienceHealth educationNursingPolitical scienceInternational healthComputer sciencePublic health

Abstract

fetched live from OpenAlex

OBJECTIVES: To explore whether education in health/medical informatics should continue to evolve along the lines pursued since the early seventies, or whether a change is advisable. METHODS: Roots and key resulting characteristics for European and US American approaches HI education are identified. In Europe holistic approaches based on a synthesis of medicine and informatics (= computer science) with programs ranging from vocational training through university programs to doctoral and postdoctoral programs were characteristic. The US American approaches emphasized the higher levels of education and a diverse selection of specialized subjects. Changes in health and health informatics are summarized. RESULTS: Two types of changes are identified: high-tech applications arising at the interface of imaging, robotics, and the -omics (genomics, proteomics, metabolomics), and invasive applications centering on consumer health informatics and a move from curative to prospective health care. CONCLUSIONS: It is proposed that curative medicine is adequately served by current educational approaches, but that the move towards prospective health care requires a move towards education and change management for health professionals and health informatics professionals.

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)0.020
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: none
Teacher disagreement score0.655
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.535
Teacher spread0.454 · 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

Citations20
Published2006
Admission routes1
Has abstractyes

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