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Record W2724252203 · doi:10.18192/uojm.v7i1.1944

Transforming Community Cancer Care: The Ottawa Regional Cancer Foundation’s Cancer Coaching Practice

2017· article· en· W2724252203 on OpenAlexaffvenueabout
Linda Eagen, J Levesque

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsOttawa Regional Cancer Foundation
Fundersnot available
KeywordsCancerMedicineHealth careGerontologyFamily medicinePolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

Community services are an increasingly important part of the healthcare landscape. Services that work to empower patients and their caregivers are having a positive impact on health outcomes and helping to reduce per capita costs of healthcare. According to the 2015 Canadian Cancer Statistics Report, by 2030 the annual number of new cancer cases in Canada is expected to increase by 79% [1]. Community-based cancer services, in particular, are urgently required to meet the growing demand for care as the complexity of the disease and its treatment continues to grow. RÉSUMÉ Les services communautaires jouent un rôle de plus en plus important dans les soins de santé. Les services qui veillent à habiliter les patients et leurs proches aidants ont un effet positif sur les résultats en matière de santé et aident à réduire les coûts par personne des soins de santé. D’ici 2030, le nombre annuel de nouveaux cas de cancer au Canada devrait augmenter de 79 % selon le rapport Statistiques canadiennes sur le cancer 2015 [1]. Des services communautaires pour le traitement du cancer, notamment, sont requis de toute urgence afin de répondre à la demande croissante de soins, alors que la complexité de la maladie et de son traitement ne cesse de croître.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.091
GPT teacher head0.440
Teacher spread0.349 · 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
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

Citations4
Published2017
Admission routes3
Has abstractyes

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