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Record W6996683134

Soins oncologiques aux Territoires du Nord-Ouest

2024· article· fr· W6996683134 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed Central · 2024
Typearticle
Languagefr
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsSocial assistanceHealth care management
DOInot available

Abstract

fetched live from OpenAlex

Situés au nord du 60(e) parallèle, les Territoires du Nord-Ouest (TNO) sont l’un des trois territoires du Canada. L’Administration des services de santé et des services sociaux des Territoires du Nord-Ouest (ASTNO), créée en 2016 pour regrouper les administrations régionales existantes à l’époque, constitue la plus vaste administration de santé du territoire. J’œuvre à titre d’infirmière en oncologie aux TNO depuis mes débuts dans le métier il y a 11 ans. Pendant cette période, l’équipe de soins oncologiques de l’ASTNO s’est agrandie et a travaillé dur pour accroître l’accès à des ressources de soutien et à des soins cohérents, coordonnés et de grande qualité, ce qui a grandement amélioré l’expérience et l’état de santé des patients ténois atteints de cancer. Le présent article raconte l’évolution des soins oncologiques et de la navigation des patients aux TNO.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.942

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.071
GPT teacher head0.335
Teacher spread0.264 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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