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Record W3005236094 · doi:10.12927/hcpol.2019.26072

Envisioning Implementation of a Personalized Approach in Breast Cancer Screening Programs: Stakeholder Perspectives

2019· article· en· W3005236094 on OpenAlexafffundvenueabout
Daphne Esquivel-Sada, Emmanuelle Lévesque, Julie Hagan, Bartha Maria Knoppers, Jacques Simard

Bibliographic record

VenueHealthcare policy · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversité LavalMcGill Genome Centre
FundersGovernment of CanadaCanadian Institutes of Health ResearchFondation du cancer du sein du QuébecGenome Canada
KeywordsPersonalized medicineStakeholderBreast cancerPersonalizationHealth carePerspective (graphical)Computer scienceKnowledge managementMedicineCancerBioinformaticsPublic relationsWorld Wide WebPolitical scienceArtificial intelligenceInternal medicine

Abstract

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Background: Advances in genomics and epidemiology can foster the implementation of a riskbased approach to current age-based breast cancer screening programs.This personalized approach would challenge the trajectory for women in the healthcare system by adding both a risk-assessment step (including a genomic test) and screening options.Objective: The aim of this study is to explore, from an organizational perspective, the acceptability of different proposals for each step of the trajectory for women in the healthcare system should a personalized approach be implemented in the province of Quebec.Methods: We interviewed 20 professional stakeholders who are either involved in the current breast cancer screening program in Quebec or who are likely to play a role in the future implementation of a personalized risk-based approach.Results and discussion: Preferences are split between proposals supporting self-management by the women themselves (e.g., solicitation through media campaign, self-collection of information and sample and results provided by letter) and proposals prioritizing more interaction between women and healthcare providers (e.g., solicitation by health professionals, collection of information and samples by a nurse and results provided by health professionals). RésuméContexte : Les avancées de la science en génomique et en épidémiologie pourraient favoriser l'implantation d' une approche basée sur le risque dans les programmes de dépistage du cancer du sein qui sont actuellement basés sur l'âge.Cette approche personnalisée poserait des défis à la trajectoire des femmes dans le système de santé en ajoutant à la fois une étape d'évaluation du risque (incluant un test génomique) et des options de dépistage.Objectif : L' objectif de cette étude est d' explorer, dans une perspective organisationnelle, l' acceptabilité de différentes propositions pour chaque étape de la trajectoire des femmes dans le système de santé si une telle approche était implantée dans la province de Québec.Méthode : Nous avons mené des interviews auprès de 20 acteurs du milieu de la santé qui sont impliqués dans le programme actuel de dépistage du cancer du sein au Québec ou qui seraient appelés à jouer un rôle advenant l'implantation d' une approche personnalisée basée sur le risque.Résultats et discussion : Les préférences des interviewés sont partagées entre les propositions qui favorisent l' autogestion par les femmes elles-mêmes (p.ex.sollicitation par des campagnes médiatiques, collecte autonome d'information et d'échantillon, résultat transmis par lettre) et les propositions qui favorisent plus d'interactions entre les femmes et les fournisseurs de soins (p.ex.sollicitation par des professionnels de la santé, collecte d'information et d'échantillon par une infirmière et résultat transmis par un professionnel de la santé).

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)0.022
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 score0.216
Threshold uncertainty score0.430

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0170.011
Scholarly communication0.0100.004
Open science0.0030.006
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.043
GPT teacher head0.375
Teacher spread0.333 · 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

Citations26
Published2019
Admission routes4
Has abstractyes

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