A pilot study of an online universal school-based intervention to prevent alcohol and cannabis use in the UK
Bibliographic record
Abstract
OBJECTIVES: The online universal Climate Schools intervention has been found to be effective in reducing the use of alcohol and cannabis among Australian adolescents. The aim of the current study was to examine the feasibility of implementing this prevention programme in the UK. DESIGN: A pilot study examining the feasibility of the Climate Schools programme in the UK was conducted with teachers and students from Year 9 classes at two secondary schools in southeast London. Teachers were asked to implement the evidence-based Climate Schools programme over the school year with their students. The intervention consisted of two modules (each with six lessons) delivered approximately 6 months apart. Following completion of the intervention, students and teachers were asked to evaluate the programme. RESULTS: 11 teachers and 222 students from two secondary schools evaluated the programme. Overall, the evaluations were extremely positive. Specifically, 85% of students said the information on alcohol and cannabis and how to stay safe was easy to understand, 84% said it was easy to learn and 80% said the online cartoon-based format was an enjoyable way to learn health theory topics. All teachers said the students were able to recall the information taught, 82% said the computer component was easy to implement and all teachers said the teacher's manual was easy to use to prepare class activities. Importantly, 82% of teachers said it was likely that they would use the programme in the future and recommend it to others. CONCLUSIONS: The Internet-based universal Climate Schools prevention programme to be both feasible and acceptable to students and teachers in the UK. A full evaluation trial of the intervention is now required to examine its effectiveness in reducing alcohol and cannabis use among adolescents in the UK before implementation in the UK school system.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".