Comparing two different approaches to measuring drug use within the same survey.
Bibliographic record
Abstract
Respondents to the National Population Health Survey in Canada (1996 97) were asked two types of questions about drug use that allowed a comparison of the responses. The first question was about self-reported drug use categories: "In the past month, did you take [e.g., antidepressants]?" The second asked about specific drugs: "What specific medications did you take over the last two days?" Responses to the latter were coded according to the main chemical entity and then grouped in specific drug product categories similar to the first question's self- reported categories. The two sets of drug use categories were cross-tabulated for the 62,588 respondents who were 20 years of age and older. The proportion of persons who reported taking specific drugs who had not previously answered "yes" to the question related to the corresponding self-reported drug use category ranged from a low of 4.8% for insulin/oral hypoglycemics to a high of 43.7% for narcotic analgesics. Various reasons for these discrepancies are discussed. A series of logistic regression models relating the discrepancies to respondent characteristics shows that there is no clear pattern of variables associated with the discrepancies. These results show that surveys should be carefully planned to reflect the type of information needed.
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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 | 0.085 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".