A matter of perspective: The convergent and incremental validity of informant‐reported drinking motives
Bibliographic record
Abstract
INTRODUCTION: Drinkers have social and affective reasons for using alcohol ('drinking motives'). Historically, drinking motives are self-reported. Informant-reports of drinking motives may be useful in corroborating self-report data. Thus, we investigated the correspondence between self- and informant-reports of drinking motives and the incremental validity of informant-reported motives in predicting targets' future alcohol problems. METHODS: Measures were completed by 174 university-aged, same-sex drinking buddy dyads (66% women) across two waves separated by 30 days. Dyad members who contacted study organisers were treated as targets, and their buddies as informants. Targets self-reported their own drinking motives at baseline, as well as their own alcohol problems at baseline and 30 days later. Informants reported on targets' drinking motives at baseline. RESULTS: Self- and informant-reports of targets' internal drinking motives (coping-depression and enhancement) showed significant, small positive correlations. Informants-reports of these same internal drinking motives (as well as coping-anxiety) predicted change in targets' alcohol problems over time, thereby providing additional predictive validity beyond that provided by targets' self-reports. DISCUSSION AND CONCLUSIONS: We encourage incorporating informant-reported internal drinking motives when assessing risk for escalating problem drinking in emerging adult drinkers.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".