PROBLEMS WITH THE GRADUATED FREQUENCY APPROACH TO MEASURING ALCOHOL CONSUMPTION: RESULTS FROM A PILOT STUDY IN TORONTO, CANADA
Bibliographic record
Abstract
AIMS: To evaluate advantages and disadvantages of the graduated frequency (GF) approach, which asks about the frequency of alcohol consumption at mutually exclusive quantity levels (i.e. 12 or more drinks, at least eight drinks but less than 12, etc.). METHODS: Telephone survey of 464 adults aged 18 and older in Toronto, Canada, using random digit dialing and computer-assisted telephone interviewing. RESULTS: Respondents reported higher frequency and volume of drinking on the GF compared to overall and beverage-specific quantity-frequency type measures; however, at least 16% of GF responses included double counting on their frequency estimates using the GF. When these cases were excluded or corrected, differences between the GF and quantity-frequency measures mostly disappeared. The GF was superior to quantity-frequency measures for identifying heavy episodic drinkers. However, the GF had little advantage over the weekly recall method except for identifying very infrequent (i.e. less often than twice a month) heavy drinkers. CONCLUSIONS: Because the GF has a high rate of response errors in terms of measuring frequency of alcohol consumption, other combinations of measures, including alternate measures of heavy episodic drinking should be considered.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".