Combining cycles of the Canadian Community Health Survey.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A single cycle of the Canadian Community Health Survey (CCHS) may not meet researchers' analytical needs. This article presents methods of combining CCHS cycles and discusses issues to consider if these data are to be combined. An empirical example illustrates the proposed methods. DATA AND METHODS: Two methods can be used to combine CCHS cycles: the separate approach and the pooled approach. With the separate approach, estimates are calculated for each cycle separately and then combined. The pooled approach combines data at the micro-data level, and the resulting dataset is treated as if it is a sample from one population. RESULTS: For the separate approach, it is recommended that the simple average of the estimates be used. For the pooled approach, it is recommended that weights be scaled by a constant factor where a period estimate covering the time periods of the individual cycles can be created. The choice of method depends on the aim of the analysis and the availability of data. INTERPRETATION: Combining cycles should be considered only if the most current period estimates do not suffice. Both methods will obscure cycle-to-cycle trends and will not reveal changing behaviours related to public health initiatives.
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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 | 0.082 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it