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Record W4378782133 · doi:10.1177/08404704231175024

Development of a consortium to examine organ donation legislative and system reforms: A report from the LEADDR consortium

2023· article· en· W4378782133 on OpenAlexafffundabout
Cynthia Isenor, Matthew J. Weiss, Stephen Beed, Robin Urquhart, Karthik Tennankore, Amanda Lucas, Gail Tomblin-Murphy, Kristina Krmpotic, Sonny Dhanani, Victoria Sullivan, Lori J. West OC, Patricia Gongal, David Hartell, Christy Simpson, Lee James, Alain Landry, Jade Dirk

Bibliographic record

VenueHealthcare Management Forum · 2023
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsCanadian Blood ServicesChildren's Hospital of Eastern OntarioUniversity of OttawaIzaak Walton Killam Health CentreMcMaster UniversityTranslational Research in OncologyDalhousie UniversityUniversity of AlbertaUniversité LavalNova Scotia Health Authority
FundersHealth Canada
KeywordsNova scotiaOrgan donationLegislatureLegislationMultidisciplinary approachJurisdictionReferralDonationVariety (cybernetics)Public administrationPolitical scienceInformed consentMedicineFamily medicineTransplantationLawGeographyAlternative medicineSurgery

Abstract

fetched live from OpenAlex

In April 2019, the province of Nova Scotia became the first jurisdiction in North America to pass legislation that incorporated deemed consent for deceased organ donation. The reform included many other important updates, including the hierarchy for consent, enabled donor and recipient contact, and mandatory referral of potential deceased donors. Additionally, system reforms were implemented to improve the deceased donation system in Nova Scotia. A collection of national colleagues identified the magnitude of the opportunity to develop a comprehensive strategy to measure and evaluate the impact of the legislative and system reforms. This article describes the successful development of a consortium from both national and provincial jurisdictions that included experts from a variety of backgrounds and clinical and administrative disciplines. In describing the creation of this group, we hope to offer our case example as a model for the evaluation of other health system reforms from a multidisciplinary perspective.

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)0.113
metaresearch head score (Gemma)0.051
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: Other · Consensus signal: none
Teacher disagreement score0.191
Threshold uncertainty score0.597

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1130.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0100.005
Scholarly communication0.0100.003
Open science0.0030.011
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.303
Teacher spread0.268 · 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
GenreOther

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

Citations0
Published2023
Admission routes3
Has abstractyes

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