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Half a Century of Wilson & Jungner: Reflections on the Governance of Population Screening

2020· preprint· en· W4246134456 on OpenAlexafffund
Steve Sturdy, Fiona A. Miller, Stuart Hogarth, Natalie Armstrong, Pranesh Chakraborty, Céline Cressman, Mark Dobrow, Kathy Flitcroft, David C. Grossman, Russell Harris, Barbara Hoebee, Kelly Holloway, Linda Kinsinger, Marlene Krag, Olga Löblová, Ilana Löwy, Anne Mackie, John K. Marshall, Jane O’Hallahan, Linda Rabeneck, Angela E. Raffle, Lynette Reid, Graham Shortland, R. Steele, Beth A. Tarini, Sian Taylor‐Phillips, Bernie Towler, Nynke van der Veen, Marco Zappa

Bibliographic record

VenueWellcome Open Research · 2020
Typepreprint
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsDalhousie UniversityCancer Care OntarioChildren's Hospital of Eastern OntarioUniversity of Toronto
FundersEuropean Research CouncilCanadian Institutes of Health ResearchCenters for Disease Control and PreventionMultiple System Atrophy CoalitionAustralian GovernmentWellcome Trust
KeywordsCorporate governancePopulationPolitical sciencePublic relationsBusinessMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Background: In their landmark report on the “Principles and Practice of Screening for Disease” (1968), Wilson and Jungner noted that the practice of screening is just as important for securing beneficial outcomes and avoiding harms as the formulation of principles. Many jurisdictions have since established various kinds of “screening governance organizations” to provide oversight of screening practice. Yet to date there has been relatively little reflection on the nature and organization of screening governance itself, or on how different governance arrangements affect the way screening is implemented and perceived and the balance of benefits and harms it delivers. Methods: An international expert policy workshop convened by the three lead authors. Results: While effective governance is essential to promote beneficial screening practices and avoid attendant harms, screening governance organizations face enduring challenges. These challenges are social and ethical as much as technical. Evidence-based adjudication of the benefits and harms of population screening must take account of factors that inform the production and interpretation of evidence, including the divergent professional, financial and personal commitments of stakeholders. Similarly, when planning and overseeing organized screening programs, screening governance organizations must persuade or compel multiple stakeholders to work together to a common end. Screening governance organizations in different jurisdictions vary widely in how they are constituted, how they relate to other interested organizations and actors, and what powers and authority they wield. Yet we know little about how these differences affect the way screening is implemented, and with what consequences. Conclusions: Systematic research into how screening governance is organized in different jurisdictions would facilitate policy learning to address enduring challenges. Even without such research, informal exchange and sharing of experiences between screening governance organizations can deliver invaluable insights into the social as well as the technical aspects of governance.

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)0.041
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.041
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.065
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.047
Scholarly communication0.0150.025
Open science0.0030.010
Research integrity0.0190.029
Insufficient payload (model declined to judge)0.0070.002

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 head0.615
GPT teacher head0.625
Teacher spread0.010 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations12
Published2020
Admission routes2
Has abstractyes

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