Advocacy for improved response to self-injury in schools: A call to action for school psychologists.
Bibliographic record
Abstract
Over the past several years, nonsuicidal self-injury (NSSI) has emerged as a widespread concern in school settings worldwide. However, despite significant strides in NSSI research, there remains a substantial knowledge gap with respect to what school staff know. Unfortunately, this can contribute to stigma and ineffective responding when working with students who self-injure. In light of its high rates and the risks with which NSSI associates, including death by suicide, this is worrisome. Accordingly, there is a pressing need for advocacy in schools to ensure that NSSI is prioritized and for proper knowledge and training be offered to school staff. The current article serves as a call to action for school psychologists as leaders and advocates in meeting these needs. We begin by articulating the central issues pertinent to low NSSI literacy and high NSSI stigma in schools, followed by a series of research-informed recommendations for timely and effective advocacy. By virtue of undertaking these initiatives, school staff will be better able to respond to the needs of youth who self-injure and advocate for them. This, in turn, can foster an enhanced school climate and greater student well-being. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.033 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.023 | 0.044 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".