I am what I am: A meta-analysis of the association between substance user identities and substance use-related outcomes.
Bibliographic record
Abstract
OBJECTIVE: Research indicates that a substance user identity (i.e., drinking, smoking, and marijuana identity) is positively correlated with substance use-related outcomes (e.g., frequency, quantity, consequences, and disorder symptoms). The current study aimed to meta-analytically derive single, weighted effect size estimates of the identity-outcome association as well as to examine moderators (e.g., substance use type, explicit/implicit assessment, demographic characteristics, and research design) of this association. METHOD: Random effects meta-analysis was conducted on 70 unique samples that assessed substance user identity and at least one substance use-related outcome (frequency, quantity, consequences, and/or disorder symptoms), and provided the necessary information for effect size calculations. RESULTS: (tobacco or marijuana). In terms of moderators of the identity-outcome association, the link between explicit drinking identity and alcohol use-related outcomes appeared to be stronger in magnitude than the relationship between implicit drinking identity and alcohol use-related outcomes; however, this difference appears to be largely due to the finding that implicit measures have lower reliability. The strongest identity-outcome association was observed among younger individuals. CONCLUSIONS: Substance user identity is clearly an important correlate of substance use-related outcomes and this association is stronger among younger individuals. Additional theoretical, empirical, and intervention research is needed to utilize knowledge gleaned from the current study on the identity-outcome association. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.030 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".