Combining cycles of the Canadian Community Health Survey.
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".