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Record W6921662178 · doi:10.7939/dvn/10591

Canadian Heart Health Survey

2015· dataset· en· W6921662178 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2015
Typedataset
Languageen
Field
Topic
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHeart diseaseDiseasePublic healthHealth informationStroke (engine)Coronary heart disease

Abstract

fetched live from OpenAlex

The Canadian Heart Health Database (CHHDB) is an integration of data from ten Provincial Heart Health Surveys conducted between 1986 and 1992. The Provincial Heart Health Surveys were conducted as part of the Canadian Heart Health Initiative and have been a collaborative effort among provincial departments of health, Health Canada and The Heart and Stroke Foundation of Canada. The origin of the collaborative approach to cardiovascular disease prevention (CVD) lies in a report prepared by the Federal Provincial Working Group on Cardiovascular Disease Prevention and Control. The Canadian Heart Health Database consists of two sets of integrated data: core information collected by all ten provincial surveys, and family history information collected by only four provinces — Quebec, Ontario Saskatchewan and Alberta. The objective of the surveys was to estimate at the provincial level, the prevalence of CVD risk factors, the knowledge and awareness levels of CVD causes, the consequences of CVD, and the associated risk factors and lifestyle behaviours.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.065
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.016
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0040.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0310.013

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 head0.061
GPT teacher head0.332
Teacher spread0.271 · 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 designObservational
Domainnot available
GenreDataset

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
Published2015
Admission routes2
Has abstractyes

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