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Record W2535952146 · doi:10.1109/fskd.2016.7603381

Development of the health decision support system (HDSS) in Canada and its implications in China

2016· article· en· W2535952146 on OpenAlexfundaboutno aff
Hongpu Hu, Tao Dai, Xing Gao, Quan Chen, Xingyun Lei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsnot available
FundersHealth CanadaNational Social Science Fund of ChinaNational Natural Science Foundation of China
KeywordsDecision support systemClinical decision support systemGovernment (linguistics)StandardizationChinaBusinessKnowledge managementProcess managementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Based on information systems and government's emphasis on efficient medical service and health management, Canadian health decision support system stands in a leading position. This paper firstly researches the Canadian health decision overall development situation, clinical decision support system, public decision support system, application features etc. through the literature research method. And then summarize the experiences and characters of Canadian decision support system construction and application situation. Finally by the analysis of development of the HDSS in Canada, some enlightens may be obtained to improve HDSS of China: (1) The establishment of Infoway is an important factor in the development of the Canadian health decision support system; (2) The Canadian government's support is the main power in the development of the Canadian health decision support system; (3) Improving the management system is an important undertaking for the Canadian health decision support system; (4) Strong financial security is the key to the development of the Canadian health decision support system; (5) Overall planning and standardization are the foundations of Canada's health decision support system development.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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