Difficulties with telephone‐based surveys on alcohol consumption in high‐income countries: the Canadian example
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
Accurate information concerning alcohol consumption level and patterns is vital to formulating public health policy. The objective of this paper is to critically assess the extent to which survey design, response rate and alcohol consumption coverage obtained in random digit dialling, telephone-based surveys impact on conclusions about alcohol consumption and its patterns in the general population. Our analysis will be based on the Canadian Alcohol and Drug Use Monitoring Survey (CADUMS) 2008, a national survey intended to be representative of the general population. The conclusions of this paper are as follows: (1) ignoring people who are homeless, institutionalized and/or do not have a home phone may lead to an underestimation of the prevalence of alcohol consumption and related problems; (2) weighting of observations to population demographics may lead to a increase in the design effect, does not necessarily address the underlying selection bias, and may lead to overly influential observations; and (3) the accurate characterization of alcohol consumption patterns obtained by triangulating the data with the adult per capita consumption estimate is essential for comparative analyses and intervention planning especially when the alcohol coverage rate is low like in the CADUMS with 34%.
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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 | 0.036 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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