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Record W2902669977 · doi:10.1080/14636778.2018.1549477

Organizational challenges to equity in the delivery of services within a new personalized risk-based approach to breast cancer screening

2018· article· en· W2902669977 on OpenAlexafffundabout
Emmanuelle Lévesque, Julie Hagan, Bartha Maria Knoppers, Jacques Simard

Bibliographic record

VenueNew Genetics and Society · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversité LavalMcGill University
FundersCanadian Institutes of Health ResearchGovernment of CanadaGénome QuébecGenome Canada
KeywordsEquity (law)MammographyInclusion (mineral)Breast cancerBreast cancer screeningBusinessKnowledge managementMedicinePublic relationsComputer sciencePolitical sciencePsychologyCancer

Abstract

fetched live from OpenAlex

Emerging evidence opens new possibilities to improve current breast cancer mammography screening programs. One promising avenue is to tailor mammography screening according to individual risk. However, some factors could challenge the implementation of such approach, specifically its potential impact on the equitable delivery of services. This study aims to identify the barriers and facilitators to the equitable delivery of services within a future integration of a personalized approach in the Québec screening program. We then propose different means to address them. We conducted 16 semi-structured interviews with stakeholders with a role in the management, implementation or assessment of the Québec screening program. The barriers and facilitators identified by respondents were regrouped in two themes: 1) Reproduction of social inequities, and 2) Amplification of regional disparities in access to services. We consider that fostering inclusion through communication strategies and relying on electronic communication technologies could help in addressing these issues.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.092
GPT teacher head0.347
Teacher spread0.254 · 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.

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

Citations23
Published2018
Admission routes3
Has abstractyes

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