Proceedings of the Survey Methods Section IMPUTATION OF PROXY RESPONDENTS IN THE CANADIAN COMMUNITY HEALTH SURVEY
Bibliographic record
Abstract
Between September 2000 and November 2001, the Canadian Community Health Survey (CCHS) collected information on the health of Canadians. The sample contained over 130,000 respondents distributed among 136 health regions in Canada in order to produce reliable estimates at the health region level. Among the respondents, a small percentage were proxy respondents, that is, another person in the household answered on behalf of the selected person. Given the private or personal nature of certain topics in the survey, several questions were not asked by proxy. Therefore, a non-negligible amount of information was missing for these respondents. Considering the magnitude of the situation in some health regions, an imputation of the missing data using a nearest neighbour approach was developed. This article describes the adopted imputation strategy as well as results of simulations done to assess its efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.448 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".