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Record W2994860631 · doi:10.9778/cmajo.20190102

Supporting women at average risk to make informed decisions about mammography when there is no “right” answer: a qualitative citizen deliberation study

2019· article· en· W2994860631 on OpenAlexvenueaboutno aff
Laura Tripp, Julia Abelson

Bibliographic record

VenueCMAJ Open · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsDeliberationContext (archaeology)MammographyPopulationInformed consentGrey literatureMedicineFamily medicinePublic relationsMedical educationMEDLINEPolitical scienceBreast cancerGeographyAlternative medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Women are encouraged to make informed choices about mammography screening that align with their values and preferences, yet information materials developed by screening programs rarely provide complete, balanced information about screening. Through a series of deliberations with Ontario citizens, we elicited perspectives on materials developed by screening programs to support informed decision-making. METHODS: We held 4 deliberative engagement events with citizens to discuss the current evidence about mammography and informed decision-making for the general population (i.e., women not at high risk) in the context of organized screening programs. Participants reviewed and provided feedback on the educational materials currently produced by screening programs in 8 provinces (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec, Nova Scotia and Newfoundland and Labrador) and 2 territories (Yukon Territory and Northwest Territory) and identified the key features that should guide the design of these materials to optimally support informed decision-making. RESULTS: In general, participants viewed the educational materials as insufficient to support informed decision-making. They identified the following key features of optimal educational materials: they should be accessible, complete and accurate, and provide information on both benefits and risks of screening in a comprehensive, easy-to-understand manner. Information materials should evoke the trust of the reader, and they should be consistent across Canada. INTERPRETATION: Canadian women have insufficient access to reliable information sources and complete evidence about mammography screening, and, without this information, they are unable to make fully informed decisions. Canadian breast screening programs must take steps to improve the information shared with women to support informed decision-making that aligns with women's values and preferences.

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)0.031
metaresearch head score (Gemma)0.042
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 score0.150
Threshold uncertainty score0.297

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0160.013
Scholarly communication0.0040.004
Open science0.0030.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.064
GPT teacher head0.412
Teacher spread0.348 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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