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Record W6931651226 · doi:10.5683/sp3/w42q5x

Synthetic: Canadian Community Health Survey, 2009: Full Sample File [Canada]

2010· dataset· en· W6931651226 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2010
Typedataset
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsData fileCommunity healthSample (material)PopulationPopulation healthAmerican Community SurveyHealth dataTest (biology)

Abstract

fetched live from OpenAlex

PLEASE NOTE: This is a Synthetic data file, also known as a Dummy File - it is NOT real data. This synthetic data file should not be used for purposes other than to develop and test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical packing 'code' (e.g. SPSS syntax, SAS programs, etc.) in preparation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Research Data Centres. <br> <br> The Canadian Community Health Survey (CCHS) is a cross-sectional survey that collects information related to health status, health care utilization and health determinants for the Canadian population. Starting in 2007, the CCHS now operates using continuous collection. It is a large sample, general population health survey, designed to provide reliable estimates at the health region level. <br> <br> In order to provide researchers with a means to access the master file(s), a remote access facility has been implemented. Remote access provides researchers with the possibility to submit computer programs via e-mail to a dedicated address <a href="mailto:cchs-escc@statcan.ca">(cchs-escc@statcan.ca)</a>, and to receive the results by return e-mail. To obtain remote access privileges, it is necessary that researchers obtain advance approval from the Health Statistics Division. Requests must be submitted to the aforementioned e-mail address and must provide the following, clearly itemized information: <br> <br> •the researcher’s affiliation, <br> • the name of all researchers involved in the project, <br> • the title of the research project, <br> • an abstract of the project, <br> • the goals of the research, <br> • the data to which access is required (survey, cycle), <br> • why the project requires access to the master data rather than the PUMF, <br> • why Remote Access service is chosen rather the on-site access in a Research Data Centre (RDC), <br> • the expected results, and <br> • the project’s expected completion date. <br> <br> Further information is available by contacting the CCHS team at the above e-mail address or by phone at (613) 951-1653. Once the request for remote access has been approved, the researcher can submit his/her computer programs to the CCHS team for processing on the master file(s). The computer output is reviewed by the team for confidentiality concerns and returned to the researcher. However, the correctness and accuracy of each program submission remains, at all times, the sole responsibility of the researcher.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.059
GPT teacher head0.297
Teacher spread0.238 · 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.

Study designNot applicable
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
Published2010
Admission routes2
Has abstractyes

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