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Record W2322858489 · doi:10.5603/imh.2015.0005

Telemedicine at sea and onshore: divergences and convergences

2015· review· en· W2322858489 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Maritime Health · 2015
Typereview
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsUniversité LavalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsTelemedicineHealth careMedicineMedical emergencyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Telemedical Maritime Assistance Service (TMAS) is one of the fundamental components of medical assistance delivery at sea. However, while onshore telemedicine is undergoing a fast growth, these research and clinical investments unfortunately did not yet benefit for telemedicine at sea. DIVERGENCES BETWEEN TELEMEDICINE AT SEA AND ONSHORE: While telemedicine aims at providing distant health care, telemedicine at sea and onshore bear major differences, particularly for merchant vessels, and to a lesser extent for passenger vessels, which can be divided between structural differences, differences of practices, and policy differences. CONVERGENCES BETWEEN TELEMEDICINE AT SEA AND ONSHORE: Despite the existence of important divergences between telemedicine at sea and telemedicine onshore, these two major branches of distant health care delivery still converge in some respects. CONCLUSIONS: Identifying the convergences between telemedicine at sea and telemedicine onshore might contribute to increase and optimise the transfer from research on onshore telemedicine to maritime telemedicine, and to overcome the relatively low amount of research performed on telemedicine at sea compared to its onshore counterpart.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.346
Teacher spread0.300 · 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