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Record W2114448636 · doi:10.1258/1357633054471894

E-health and the Universitas 21 organization: 4. Professional portability

2005· article· en· W2114448636 on OpenAlexaff
Michael A. Goldberg, Zena Sharman, Brandi Bell, Kendall Ho, Niv Patil

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsConcordia UniversityUniversity of British Columbia
Fundersnot available
KeywordsSoftware portabilitySWOT analysisRelevance (law)Health careProfessional developmentQuality (philosophy)Public relationsBusinessMedical educationMedicineKnowledge managementPolitical scienceComputer scienceMarketing

Abstract

fetched live from OpenAlex

Professional portability is the ease with which health-care professionals can move in person or virtually across barriers, and among and between jurisdictions, to transfer their knowledge, skills and care. As part of the Universitas 21 (U21) project on e-health, professional portability was examined using a SWOT analysis (strengths, weaknesses, opportunities and threats). The analysis showed that many factors hamper the development of global professional portability; on the other hand, the potential exists to substantially improve access to health care and its quality around the world. The study suggests that professional portability can be advanced in a number of ways. These include exploring policy, technology and medical training. The field of professional portability, while of considerable relevance to health and other professions, is undeveloped and is clearly an area that would benefit from discussion, research and global collaboration.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0170.001

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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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.

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

Citations8
Published2005
Admission routes1
Has abstractyes

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