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Record W1964545769 · doi:10.12927/cjnl.2008.19687

Luddite or Luminary?

2008· article· en· W1964545769 on OpenAlexaffvenueabout
Lynn Nagle

Bibliographic record

VenueNursing leadership · 2008
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsTelehealthBusinessHealth carePublic relationsInvestment (military)NursingMedicineEconomic growthTelemedicinePolitical science

Abstract

fetched live from OpenAlex

The Electronic Health Record (EHR) charge is on across the country, with increasing investments in systems infrastructure, the adoption of standards and the integration of health information systems within regions and across sectors of care. In the near term, whether they are institutionor community-based, all nurses will need to use the various functional components of EHRs. Infoway has set a direction for Canada to reach the goal of having an EHR for 50% of Canadians by 2010 (Canada Health Infoway 2007). As an investment partner, Infoway is working within all provincial and territorial jurisdictions to deploy the foundations of the Canadian EHR. Client and provider registries, laboratory, diagnostic imaging and drug information systems, public health surveillance and telehealth applications are among the key functional components. Additionally, funding is being directed to innovative technological approaches, including investments to facilitate clients’ access to care, and information and support for cancer care, mental health and primary care.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.558
Threshold uncertainty score0.631

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5580.400

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 head0.634
GPT teacher head0.482
Teacher spread0.152 · 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.

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

Citations2
Published2008
Admission routes3
Has abstractyes

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