Promising Approaches in Prevention and Intervention in Secondary School Settings
Bibliographic record
Abstract
Abstract Self-injury is not an unusual occurrence in adolescence and thus arises in secondary school settings with some frequency. However, as discussed in this chapter, the complexity of the behavior and the importance of school involvement cannot be underestimated. This chapter begins with a brief overview of the occurrence of NSSI in secondary school students, contextualizing it in terms of its prevalence, functions, stigmatization, and risk and protective factors. The developmental and contextual significance of addressing NSSI in secondary schools will then be highlighted. Following this, a review of the literature surrounding NSSI prevention/intervention approaches within secondary school settings is discussed. Recommendations for NSSI prevention and intervention in secondary school settings will then be organized by level (i.e., primary, secondary, and tertiary). Elements of both prevention and intervention are embedded within each level; specifically, (a) primary prevention/intervention aims to prevent the onset of a behavior through universal intervention; (b) secondary prevention/intervention aims to prevent the onset of a behavior in individuals at-risk; and (c) tertiary prevention/intervention aims to respond to a behavior once it has emerged to reduce its negative impacts. The chapter then concludes by discussing the challenges to NSSI prevention/intervention in secondary school settings, recommendations for navigating such challenges should they arise, and suggestions for future research in this area.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".