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Record W2147734067 · doi:10.3747/co.20.1209

When Jurisdictional Boundaries Become Barriers to Good Patient Care

2013· article· en· W2147734067 on OpenAlexafffundvenueabout
Joanne Stephen, Karen Fergus, S. Sellick, Michael Speca, Jill Taylor‐Brown, Jill Turner, Kate Collie, Deborah McLeod, Adina Rojubally

Bibliographic record

VenueCurrent Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsDalhousie UniversityThunder Bay Regional Health Sciences CentreSunnybrook Health Science CentreCancerCare ManitobaBC Cancer Agency
FundersPartenariat Canadien Contre Le Cancer
KeywordsLicensureTelehealthContext (archaeology)Public relationsMedicineHealth careNursingTelemedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Canada is a pioneer in remote cancer care delivery to underserved populations; however, it is trailing behind on policies that would support clinicians in providing care using distance technologies. The current policy framework is disjointed, and discussions by professional boards about online jurisprudence associated with licensure appear to be regressive. We hope that by addressing the discrepancies in interjurisdictional practice and focusing on the key issue of "where therapy resides," we will be able to nudge dialogue and thinking closer toward the reasoning and recommendations of national telehealth organizations. We present this discussion of jurisdictional issues and e-health practice in the context of a pan-Canadian online support program developed for cancer patients and family members. Although the present paper uses online support groups as a springboard to advocate for e-health, it ultimately addresses a broader audience: that of all Canadian health care stakeholders.

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)0.037
metaresearch head score (Gemma)0.111
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.482
Threshold uncertainty score0.959

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.111
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0210.036
Scholarly communication0.0180.013
Open science0.0040.019
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0070.001

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 head0.189
GPT teacher head0.498
Teacher spread0.310 · 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
GenreCommentary

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
Published2013
Admission routes4
Has abstractyes

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