Predictors of aging out of heavy episodic drinking in emerging adults: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Heavy episodic drinking (HED) refers to alcohol consumption that exceeds the recommended threshold for a given episode and increases risk for diverse negative alcohol-related consequences. A pattern of weekly HED is most prevalent in emerging adults (i.e., age 18-25). However, rates of HED consistently decline in the mid to late twenties, referred to as 'aging out' or 'maturing out' of HED. Although many individual studies have followed changes in drinking behaviour over the transition to adulthood, there has yet to be a systematic review to identify consistent factors contributing to risk (i.e. failure to age out) and protection (i.e. successful aging out). The objective of this review will be to summarize and critically appraise the literature on factors contributing to aging out of HED among emerging adults. METHODS: A systematic search of observational cohort studies following drinking behaviours in age cohorts overlapping with the emerging adulthood period will be conducted in MEDLINE, EMBASE, PsychInfo, and CINAHL. Two independent reviewers will evaluate identified studies for inclusion eligibility, extract study data, and assess the quality of included studies. Primary outcomes will be quantity/frequency of alcohol use (e.g. drinks/week) and severity of alcohol-related problems. Predictors of maturing out of HED will be reported narratively, and where appropriate, random effects meta-analyses will be conducted to provide pooled effect sizes. An evidence map will be created to characterize the overall pattern of findings. DISCUSSION: This systematic review will provide a timely and warranted summary of published work contributing to understanding aging out of heavy episodic drinking. Our findings will provide critical commentary on the developmental course of HED during the transition from adolescence to adulthood and will be the first review to consider both protective and risk factors for maturing out of frequent binge drinking. By highlighting factors identifying those at-risk for prolonged heavy episodic drinking, our conclusions will have important treatment implications for primary, secondary, and tertiary intervention strategies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017078436 .
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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 | 0.052 | 0.067 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.016 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.006 |
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".