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Record W2483304308 · doi:10.1186/s12919-016-0006-0

Transforming Health Care in Remote Communities: report on an international conference

2016· article· en· W2483304308 on OpenAlexafffundabout
T. Kue Young, Susan Chatwood, James D. Ford, Gwen Healey, Michael Jong, Josée G. Lavoie, Mason White

Bibliographic record

VenueBMC Proceedings · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of ManitobaHealth Sciences CentreMemorial University of NewfoundlandMcGill UniversityNOSM UniversityInstitute for Circumpolar Health ResearchPublic Health OntarioGovernment of Newfoundland and LabradorQaujigiartiit Health Research CentreUniversity of TorontoUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta InnovatesUniversity of Alberta
KeywordsCircumpolar starGeneral partnershipGovernment (linguistics)Health careMedicineLibrary sciencePublic healthPublic administrationPolitical sciencePublic relationsNursing

Abstract

fetched live from OpenAlex

An international conference titled "Transforming Health Care in Remote Communities" was held at the Chateau Lacombe Hotel in Edmonton, Canada, April 28-30, 2016. The event was organized by the University of Alberta's School of Public Health, in partnership with the Institute for Circumpolar Health Research in Yellowknife, Northwest Territories, and the Qaujigiartiit Health Research Centre in Iqaluit, Nunavut. There were 150 registrants from 7 countries: Canada (7 provinces and 3 territories), United States, Denmark, Iceland, Norway, Sweden, and Australia. They included representatives of academic institutions, health care agencies, government ministries, community organizations, and private industry. The Conference focused on developing solutions to address health care in remote regions. It enabled new networks to be established and existing ones consolidated.

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)0.015
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0100.002
Scholarly communication0.0110.006
Open science0.0030.015
Research integrity0.0070.012
Insufficient payload (model declined to judge)0.0240.002

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 head0.099
GPT teacher head0.420
Teacher spread0.321 · 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
GenreOther

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
Published2016
Admission routes3
Has abstractyes

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