Substance misuse prevention among School Aged students
Bibliographic record
Abstract
Problem School aged children are particularly at higher risk for mental health and substance misuse related problems, more so since COVID 19 epidemic. Environmental risk factors have been identified by SAMHSA such as poverty, divorce, peer drug use, early aggressive behavior among many others, are cumulative over a person’s lifetime with respect to behavioral health. As risk factors accumulate and protective factors correspondingly decrease, children experience higher rates of substance misuse, depression, anxiety, and self-harm. Literature Review The literature review covers two main topics: effectiveness of prevention programs; schools-community partnerships. Peer education models have had success with enhancing student knowledge about addiction as well as increasing levels of self-efficacy perception. One example of a successful partnership is the FACES program in Ontario. Results from the program showed a positive impact on community engagement for participating stakeholders, as well as a smoother transition for the children. Program Implementation and Evaluation Methodology 9 KSU students were trained in in SPF (Strategic Prevention Framework) model, Mindfullness and Sources of Strength. Two schools with very diverse student populations were selected to partner with KSU for these three interventions. Students from one school were trained in SPF and Mindfullness; students from the second school were trained in SPF and Sources of Strength. This presentation will provide an overview of the pre-post test results from the SPF training since the post-test from Mindfullness and Sources of Strength will occur in May. Also, the presentation will provide an overview of the partnership and action-based research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".