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Record W4320005458 · doi:10.1109/ojemb.2023.3241597

Building a One Country One Licensure Framework: Applications for the Future of Canadian Space Physicians

2023· article· en· W4320005458 on OpenAlexaffabout
Alex Zhou, Valerie Nwaokoro, Valerie Oosterveld, A. Sirek

Bibliographic record

VenueIEEE Open Journal of Engineering in Medicine and Biology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsWestern UniversityUniversity of Windsor
Fundersnot available
KeywordsLicensureLicenseSpace (punctuation)Health careBusinessTelemedicineCoronavirus disease 2019 (COVID-19)Public relationsMedicinePolitical scienceEconomic growthMedical educationComputer scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Space medicine provides care in the most extreme environment known to humankind. The global space economy is forecast to be a $1 trillion industry by 2040. Its increased utilization will require additional legal healthcare support frameworks. We reviewed the current Canadian medicolegal framework for the capability to adapt to this new demand. Currently, Canadian physicians are required to hold a license in each province they practice. As space medicine encompasses multiple medical specialties and its practice is beyond Canadian provincial jurisdictions, we identified medicolegal gaps in the Canadian ability to provide space healthcare. Geographical licensing restrictions have caused detriment to healthcare provision in remote communities, military medicine, and telemedicine, exacerbated by COVID-19. By examining similarities and solutions from these terrestrial situations, bi-directional translational licensing solutions may be found. Recommendations for an improved Canadian licensing framework targeting provision of space medicine may lead to improving healthcare access and universality for Canadians nationwide.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.044
GPT teacher head0.346
Teacher spread0.302 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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