Non–response bias in alcohol and drug population surveys
Bibliographic record
Abstract
INTRODUCTION AND AIMS: This proposed study was to assess non-response bias in the 2004 Canadian Addictions Survey (CAS). DESIGN AND METHODS: Two approaches were used to assess non-response bias in the CAS which had a response rate of only 47%. First, the CAS sample characteristics were compared with the 2002 Canadian Community Health Survey (CCHS, response rate 77%) and the 2001 Canada Census data. Second, characteristics of early and late respondents were compared. RESULTS: People with lowest income and less than high-school education and those who never married were under-represented in the CAS compared with the Census, but similar to the CCHS. Substance use was more prevalent in the CAS than the CCHS sample, but most of the CAS and CCHS estimates did not exceed +/-3% points. Late respondents were also significantly more likely to be male, young adult, highly educated, used, have high income, live in different provinces and report substance use. Multivariate logistic regression found significant non-response bias for lifetime, past 12 months, chronic risky, acute risky and heavy monthly alcohol use, lifetime and past year cannabis use, lifetime hallucinogen use, any illicit drug uses of lifetime and past year. Adjustment for non-response bias substantially increased prevalence estimates. For example, the estimates for lifetime and past 12 month illicit drug use increased by 5.22% and 10.34%. DISCUSSION AND CONCLUSIONS: It is concluded that non-response bias is a significant problem in substance use surveys with low response rates but that some adjustments can be made to compensate.
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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 | 0.400 | 0.614 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".