Reliability of the Timeline Followback for cocaine, cannabis, and cigarette use.
Bibliographic record
Abstract
The Timeline Followback (TLFB), a retrospective calendar-based measure of daily substance use, was initially developed to obtain self-reports of alcohol use. Since its inception it has undergone extensive evaluation across diverse populations and is considered the most psychometrically sound self-report measure of drinking. Although the TLFB has been extended to other behaviors, its psychometric evaluation with other addictive behaviors has not been as extensive as for alcohol use. The present study evaluated the test-retest reliability of the TLFB for cocaine, cannabis, and cigarette use for participants recruited from outpatient alcohol and drug treatment programs and the general community across intervals ranging from 30 to 360 days prior to the interview. The dependent measure for cigarette smokers and cannabis users was daily use of cigarettes and joints, respectively, and for cocaine users it was a "Yes" or "No" regarding cocaine use for each day. The TLFB was administered in different formats for different drug types. Different interviewers conducted the two interviews. The TLFB collected highly reliable information about participants' daily use of cocaine, cannabis, and cigarettes from 30, 90, to 360 days prior to the interview. Findings from this study not only suggest that shorter time intervals (e.g., 90 days) can be used with little loss of accuracy, but also add to the growing literature that the TLFB can be used with confidence to collect psychometrically sound information about substance use (i.e., cocaine, cannabis, cigarettes) other than alcohol in treatment- and nontreatment-seeking populations for intervals from ranging up to 12 months prior to the interview.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".