Self-reported substance use with clinician interviewers versus self-administered surveys
Bibliographic record
Abstract
OBJECTIVE: Underreporting of substance use is a frequent concern about studies based on self-report, but few robust studies have examined the agreement between different methods for capturing self-reported substance use. The current study therefore used repeated measures to compare self-reported substance use using (a) clinician interviewers and (b) self-administered computerized surveys in a sample that included both inpatients and community residents. METHOD: = 476) interviews, participants were asked whether they used alcohol, cannabis, cocaine, heroin, and prescription painkillers by two methods: semi-structured, clinician-administered interview, and computerized self-administered questionnaire. Agreement between these two methods was investigated using Cohen's kappa coefficient. Multivariable logistic regression assessed differences in the odds of discordance between the two measures by recruitment source, gender, age, race/ethnicity, employment status, marital status, and level of education. RESULTS: There was moderate-to-strong agreement between clinician-administered and self-administered surveys for alcohol (kappa = .70-.88), cannabis (kappa = .87-.92), cocaine (kappa = .81-.89), and heroin (kappa = .90-.92). However, there was only weak-to-moderate agreement for nonmedical use of prescription painkillers (kappa = .55-.71), with the self-administered questionnaire capturing a higher prevalence of use (percent difference = 2.4%). CONCLUSIONS: Clinician interviewers and self-administered surveys were shown to capture similar rates of self-reported use of alcohol, cannabis, cocaine, and heroin. Surveys assessing nonmedical prescription opioid use may benefit from using self-administered questionnaires.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".