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Record W11884840

Canadian Health Measures Survey pre-test: design, methods, results.

2007· article· en· W11884840 on OpenAlexaffabout
Mark S. Tremblay, Renée Langlois, Shirley Bryan, Dale Esliger, Julienne Patterson

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsSurvey researchSurvey methodologySurvey data collectionData collectionTest (biology)Environmental healthMedicinePsychologyApplied psychologyStatisticsMathematicsPathologyBiology
DOInot available

Abstract

fetched live from OpenAlex

The Canadian Health Measures Survey (CHMS) pre-test was conducted to provide information about the challenges and costs associated with administering a physical health measures survey in Canada. To achieve the specific objectives of the pre-test, protocols were developed and tested, and methods for household interviewing and clinic testing were designed and revised. The cost, logistics and suitability of using fixed sites for the CHMS were assessed. Although data collection, transfer and storage procedures are complex, the pre-test experience confirmed Statistics Canada's ability to conduct a direct health measures survey and the willingness of Canadians to participate in such a health survey. Many operational and logistical procedures worked well and, with minor modifications, are being employed in the main survey. Fixed sites were problematic, and survey costs were higher than expected.

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)0.024
metaresearch head score (Gemma)0.027
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: Empirical · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.027
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.009
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0040.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0230.004

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 head0.183
GPT teacher head0.419
Teacher spread0.236 · 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
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

Citations39
Published2007
Admission routes2
Has abstractyes

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