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Improving Access to Oncology Care for Individuals and Families through Telehealth

2010· book-chapter· en· W2493704466 on OpenAlexaffabout
Johanna den Duyf, Lars Apland, Arminée Kazanjian, Margarita Loyola, Sarah Robertson

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsRoyal Jubilee HospitalUniversity of British ColumbiaProvincial Health Services AuthorityBC Cancer Agency
Fundersnot available
KeywordsTelehealthGeneral partnershipTelemedicineAgency (philosophy)Health careBusinessNursingSustainabilityService (business)Service delivery frameworkInformation and Communications TechnologyMedicinePolitical scienceMarketing

Abstract

fetched live from OpenAlex

Telemedicine, or the use of information communication technology (ICT) for medical diagnosis and patient care, is an innovative method of health service delivery. It offers opportunities and challenges for clinicians, consumers and health care organizations. In British Columbia, specialized oncology health care services are provided to cancer patients at one of the five Regional Cancer Centers of the B.C. Cancer Agency (BCCA). The burden and stress of travel for rural patients as well as the increasing demand for specialized cancer care services prompted us to explore telemedicine as an alternative health service delivery method for these patients. This article will outline a research study undertaken in partnership with the Vancouver Island Health Authority (VIHA), Provincial Services Health Authority (PHSA) and the University of British Columbia. Implementation and sustainability of a telehealth program requires an examination of organizational, health care system and technical readiness. Barriers to uptake include human factors and infrastructure requirements. A systematic approach optimizes the successful implementation of a telehealth program.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.052
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0520.009

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 head0.047
GPT teacher head0.390
Teacher spread0.343 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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