Covariates in studies examining longitudinal relationships between substance use and mental health problems among youth: A meta-epidemiologic review
Bibliographic record
Abstract
INTRODUCTION: This meta-epidemiological review examines covariate selection and reporting practices in observational studies analyzing longitudinal relationships between youth substance use and mental health problems (internalizing and externalizing). METHODS: Sixty-nine studies published in high-impact journals from 2018 to 2023 were included. Studies were included if they explored prospective relationships between substance use and mental health among youth (12-25 years) and used repeated measures designs. Data extraction focused on study characteristics, covariates and their selection methods, and reporting practices. RESULTS: There were 574 covariates included across studies; 33 were included as moderators and 18 were included as mediators. At the study level, the most common covariate domains included demographics (90 % of included studies had at least one demographic, mostly sex), substance-related variables (67 %; mostly alcohol or smoking), internalizing symptoms (39 %; mostly depression), family-related variables (29 %; mostly parental substance use or mental illness), and externalizing symptoms (19 %; mostly conduct). 93 % of studies had unique sets of lower-order covariates. Across all studies (n = 69), only 35 % provided details for how, and why, all covariates were selected with only 12 % reporting selecting covariates a priori, and none being pre-registered. Only 60 % mentioned confounding and only 13 % mentioned risk of confounding in their conclusions. CONCLUSIONS: The findings highlight the need for improved covariate selection and reporting practices. Establishing a core set of covariates and adhering to standardized reporting guidelines would enhance the comparability and reliability of research findings in this field. Researchers can use this review to identify and justify the inclusion and exclusion of commonly reported covariates when analyzing relationships between youth substance use and mental health problems.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Meta-epidemiology (broad)Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.022 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".