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Record W1566162777

First Nations led telemedicine: From access to effective use

2010· article· en· W1566162777 on OpenAlexaffabout
Andres Ibanez Migeot

Bibliographic record

VenueThe Atrium (University of Guelph) · 2010
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTelemedicineInternet privacyBusinessComputer scienceTelecommunicationsEconomic growthEconomicsHealth care
DOInot available

Abstract

fetched live from OpenAlex

Starting from the observation that 'non-technological' elements matter to the introduction of new technologies into rural and remote communities, this thesis explores the organizational relationships that facilitate the development of a First Nations telemedicine system in northwestern Ontario. Accordingly, it adapts an existing "community readiness for telemedicine" framework to the description of factors that distinguish First Nations contexts for innovation with telemedicine. From this understanding, a First Nations led telemedicine effort is analyzed, giving way to the possibility that First Nations leadership in the introduction and development of a telemedicine system, which builds local First Nations capacity, is a requirement for success.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.973
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0120.009
Open science0.0010.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.016
GPT teacher head0.260
Teacher spread0.244 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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