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The Rural Learning Challenge

2015· book-chapter· en· W2494623497 on OpenAlexaffabout
Al Lauzon

Bibliographic record

VenueAdvances in medical education, research, and ethics (AMERE) book series · 2015
Typebook-chapter
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTelehealthContext (archaeology)The InternetHealth carePublic relationsRural healthTelemedicineRural areaRural managementPolitical scienceSociologyEconomic growthGeographyRural developmentWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

This chapter examines how technology is supporting the health and health care system for rural/remote people, specifically telehealth and the Internet, with a focus on the Canadian context. I will begin by outlining the opportunities and the challenges that technology presents to rural people and communities. This is followed by highlighting the divide between rural and urban in the Canadian context, with a focus on inequities related to health. This is followed by exploring the role of ICTs in health and health care with a focus on changes in the Canadian healthcare system, telehealth and the Internet as a source of health related information. These issues are then examined through a rural lens, asking the question what, if any are the implications for rural people and communities. I end with a section of reflections followed by the conclusion that ICTs present new opportunities for rural people and communities, but if they are to be able to take advantage of these opportunities they must learn to develop the necessary capacities, both as individuals and as a community. Their challenge is a learning challenge.

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)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, 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: none
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.006
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 head0.110
GPT teacher head0.497
Teacher spread0.387 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2015
Admission routes2
Has abstractyes

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