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Record W125066202 · doi:10.1155/2007/386481

Contributing to Communicable Diseases Intelligence Management in Canada: CACMID Meeting, March 2007, Halifax, Nova Scotia

2007· article· en· W125066202 on OpenAlexaffabout
Shamir Mukhi, Jeff Aramini, Amin Kabani

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2007
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsPublic healthAgency (philosophy)Nova scotiaBusinessPublic health surveillancePublic relationsPublic health informaticsPolitical scienceInternational healthHealth policyMedicineGeographySociology

Abstract

fetched live from OpenAlex

In the spring of 2003, the Public Health Agency of Canada (then, Health Canada) partnered with several provincial/territorial and regional public health stakeholders to improve pan-Canadian public health surveillance, communications and response through the application of new technologies. This resulted in the creation of the Canadian Network for Public Health Intelligence (CNPHI), a comprehensive framework of applications and resources designed to fill critical gaps in Canada's national public health infostructure. Over the past four years, the CNPHI has evolved into Canada's only pan-Canadian public health information management system. With over 2000 registered users, the current CNPHI environment consists of more than 30 integrated applications and systems that can be loosely categorized into four functional groups: data exchange; data analysis and integration; communication, collaboration and coordination; and knowledge management. Despite poor data repositories, legacy information management systems, and the lack of standards and agreements, the CNPHI has demonstrated that much can be accomplished in these areas. Over the next decade, significant barriers impeding additional advances will be bridged through the implementation of the Electronic Health Record, and through ongoing efforts to address gaps in standards, and data- and information-sharing agreements. Together with new technologies coming on-line, opportunities to further enhance public health surveillance and response will be limited only by one's imagination.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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 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

Citations16
Published2007
Admission routes2
Has abstractyes

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