New Initiatives in Data Collection for the Canadian Community Health Survey
Bibliographic record
Abstract
The Canadian Community Health Survey (CCHS) was redesigned in 2007 so that it could use the continuous data collection method. Since then, a new sample has been selected every two months, and the data have also been collected over a two-month period. The survey uses two collection techniques: computer-assisted personal interviewing (CAPI) for the sample drawn from an area frame, and computer-assisted telephone interviewing (CATI) for the sample selected from a telephone list frame. Statistics Canada has recently implemented some data collection initiatives to reduce the response burden and survey costs while maintaining or improving data quality. The new measures include the use of a call management tool in the CATI system and a limit on the number of calls. They help manage telephone calls and limit the number of attempts made to contact a respondent. In addition, with the paradata that became available very recently, reports are now being generated to assist in evaluating and monitoring collection procedures and efficiency in real time. The CCHS has also been selected to implement further collection initiatives in the future. This paper provides a brief description of the survey, explains the advantages of continuous collection and outlines the impact that the new initiatives have had on the survey.
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 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 | 0.135 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.035 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".