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Record W4404284008 · doi:10.5737/23688076344531

L’expérience d’accès aux soins de soutien au Nouveau-Brunswick (Canada) pour les survivants du cancer et leurs proches aidants

2024· article· fr· W4404284008 on OpenAlexvenueaboutno aff
Charlotte Schwarz, Alison Luke, Julia Besner, Luke MacNeill, Lauren Renée Ashfield, Julie Easley, Stephanie McIntosh-Lawrence, Shelley Doucet

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2024
Typearticle
Languagefr
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Les soins de soutien peuvent réduire la détresse et améliorer la qualité de vie des survivants du cancer et de leurs proches aidants. Malheureusement, l’accès à ces services est souvent complexe. C’est pourquoi la présente étude visait à examiner l’expérience d’accès aux soins de soutien au Nouveau-Brunswick (Canada) pour les survivants du cancer et leurs proches aidants, ainsi qu’à jauger leur intérêt éventuel envers la création d’un centre provincial de soins de soutien. En tout, 44 personnes ont participé à un sondage (en ligne ou par la poste) visant à comprendre les besoins en soins de soutien et l’expérience d’accès à ce type de services. Les résultats ont révélé quels étaient les besoins les plus importants pour les participants (c.-à-d. le soutien mental et socioaffectif). De nombreux répondants ignoraient l’existence des services de suivi et la manière d’y accéder. Les participants avaient divers besoins non satisfaits en matière de soins, notamment concernant le soutien informationnel et la coordination des soins. Tous ont dit qu’ils aimeraient avoir un centre de soutien au Nouveau-Brunswick. Ces conclusions ont permis de formuler des recommandations centrales pour améliorer la coordination et la prestation des soins de soutien pour cette population.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.005
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.053
GPT teacher head0.339
Teacher spread0.286 · 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 designQualitative
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 routes2
Has abstractyes

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Same venueCanadian Oncology Nursing JournalSame topicCancer survivorship and careFrench-language works237,207