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‘I just want to be able to make a choice’: Results from citizen deliberations about mammography screening in Ontario, Canada

2018· article· en· W2892460571 on OpenAlexafffundabout
Julia Abelson, Laura Tripp, Jonathan Sussman

Bibliographic record

VenueHealth Policy · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsJuravinski HospitalJuravinski Cancer CentreImpactMcMaster University
FundersMcMaster University
KeywordsMammographyCitizen scienceMammography screeningPolitical scienceMedicineSociologyBreast cancerBiology

Abstract

fetched live from OpenAlex

Despite Canada's long history with mammography screening, little is known about citizens' perspectives about mammography and how best to support women to make informed choices about screening. To address this gap, a series of four citizen deliberation events were held in 2015-16 in Ontario, a Canadian province with an organized population-based breast screening program in place since 1990. Forty-nine individuals participated in four citizen panels, each comprising an information session highlighting the evidence about mammography, and large- and small-group deliberations about approaches to support informed decision making for screening. Following their engagement with the research evidence about mammography, participants expressed concern about their lack of full awareness of the risks and benefits and a strong desire for choice when it comes to screening. To support informed choice, mammography programs need to reflect the values of information sharing, trust and transparency, financial accountability, and allow for personal interactions and shared decision-making. Citizens are looking for balanced information about the risks and benefits of screening presented in an easy to understand, comprehensive, and transparent manner. Primary health care providers and organized screening programs are important sources of information about mammography and must be vigilant in their efforts to support informed decision-making in this area by ensuring that the information materials they are using are balanced and reflect current evidence.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.134
GPT teacher head0.403
Teacher spread0.269 · 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 designNot applicable
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

Citations15
Published2018
Admission routes3
Has abstractyes

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