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Record W2787312307 · doi:10.5737/236880762814653

Combler le fossé entre les soins oncologiques et communautaires grâce à une évaluation pancanadienne des pratiques en vigueur

2018· article· fr· W2787312307 on OpenAlexaffvenue
Danielle Wittal

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2018
Typearticle
Languagefr
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Étant donné le nombre croissant de survivants du cancer, il devient impératif d’évaluer les soins de suivi pour bien répondre aux besoins des patients. Une évaluation des pratiques a donc été réalisée, à l’échelle provinciale et nationale, pour mieux comprendre les soins aux survivants, noter les tendances et signaler les disparités observées pendant la transition du milieu oncologique vers les soins communautaires. Des entrevues téléphoniques ont permis de recueillir les données de 9 provinces sur 10. Les documents pertinents ont également été rassemblés et les domaines de bonnes pratiques, recensés. Les façons de faire variaient grandement d’une province à l’autre et l’évaluation a permis de formuler certaines recommandations quant à l’amélioration des soins. Les résultats obtenus montrent l’importance d’encourager les patients à s’autogérer et à faire valoir leurs besoins et leurs droits. L’étude en vient à la conclusion que la survie au cancer est un sujet complexe qui nécessite communication et coordination entre les services pour assurer une transition des soins sans heurts.

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)0.036
metaresearch head score (Gemma)0.081
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 score0.986
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.081
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0030.002
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.051
GPT teacher head0.368
Teacher spread0.317 · 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
Published2018
Admission routes2
Has abstractyes

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