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Record W1986333848 · doi:10.12927/hcpol.2013.23397

Meeting the Privacy Requirements for the Development of a Multi-Centre Patient Registry in Canada: The Rick Hansen Spinal Cord Injury Registry

2013· article· en· W1986333848 on OpenAlexafffundvenueabout
Vanessa Noonan, Nancy P. Thorogood, Michael G. Fehlings, B. Catharine Craven, Gary Linassi, Daryl R. Fourney, Brian K. Kwon, Christopher S. Bailey, Eve C. Tsai, Brian Drew, Henry Ahn, Deborah Tsui, Marcel F. Dvorak

Bibliographic record

VenueHealthcare policy · 2013
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of SaskatchewanUniversity of TorontoPraxis Spinal Cord Institute
FundersCanadian Institutes of Health ResearchRick Hansen InstituteUniversity of Saskatchewan
KeywordsLegislationPersonally identifiable informationPrivacy policyInformation privacyStatuteBusinessCorporate governanceComputer securityInternet privacyPublic relationsPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

Privacy legislation addresses concerns regarding the privacy of personal information; however, its interpretation by research ethics boards has resulted in significant challenges to the collection, management, use and disclosure of personal health information for multi-centre research studies. This paper describes the strategy used to develop the national Rick Hansen Spinal Cord Injury Registry (RHSCIR) in accordance with privacy statutes and benchmarked against best practices. An analysis of the regional and national privacy legislation was conducted to determine the requirements for each of the 31 local RHSCIR sites and the national RHSCIR office. A national privacy and security framework was created for RHSCIR that includes a governance structure, standard operating procedures, training processes, physical and technical security and privacy impact assessments. The framework meets a high-water mark in ensuring privacy and security of personal health information nationally and may assist in the development of other national or international research initiatives.

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)0.003
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.017
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 science0.0010.000
Research integrity0.0000.001
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 head0.430
GPT teacher head0.551
Teacher spread0.121 · 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.

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

Citations12
Published2013
Admission routes4
Has abstractyes

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