Mentoring adolescents to prevent drug and alcohol use
Bibliographic record
Abstract
BACKGROUND: Many adolescents receive mentoring. There is no systematic review if mentoring prevents alcohol and drug use. OBJECTIVES: Assess effectiveness of mentoring to prevent adolescent alcohol/drug use. SEARCH METHODS: Cochrane CENTRAL (issue 4), MEDLINE (1950-to July 2011), EMBASE (1980-to July 2011), 5 other electronic and 11 Grey literature electronic databases, 10 websites, reference lists, experts in addictions and mentoring. SELECTION CRITERIA: Randomised controlled trials (RCTs) of mentoring in adolescents to prevent alcohol/drug use. DATA COLLECTION AND ANALYSIS: We identified 2,113 abstracts, independently assessed 233 full-text articles, 4 RCTs met inclusion criteria. Two reviewers independently extracted data and assessed risks of bias. We contacted investigators for missing information. MAIN RESULTS: We identified 4 RCTs (1,194 adolescents). No RCT reported enough detail to assess whether a strong randomisation method was used or allocation was concealed. Blinding was not possible as the intervention was mentoring. Three RCTs provided complete data. No selective reporting.Three RCTs provided evidence about mentoring and preventing alcohol use. We pooled two RCTs (RR for mentoring compared to no intervention = 0.71 (95% CI = 0.57 to 0.90, P value = 0.005). A third RCT found no significant differences.Three RCTs provided evidence about mentoring and preventing drug use, but could not be pooled. One found significantly less use of "illegal" drugs," one did not, and one assessed only marijuana use and found no significant differences.One RCT measured "substance use" without separating alcohol and drugs, and found no difference for mentoring. AUTHORS' CONCLUSIONS: All four RCTs were in the US, and included "deprived" and mostly minority adolescents. Participants were young (in two studies age 12, and in two others 9-16). All students at baseline were non-users of alcohol and drugs. Two RCTs found mentoring reduced the rate of initiation of alcohol, and one of drug usage. The ability of the interventions to be effective was limited by the low rates of commencing alcohol and drug use during the intervention period in two studies (the use of marijuana in one study increased to 1% in the experimental and to 1.6% in the control group, and in another study drug usage rose to 6% in the experimental and 11% in the control group). However, in a third study there was scope for the intervention to have an effect as alcohol use rose to 19% in the experimental and 27% in the control group. The studies assessed structured programmes and not informal mentors.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".