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Record W2991119709 · doi:10.1177/1942602x19886381

Addressing Self-Injury in Schools, Part 1: Understanding Nonsuicidal Self-Injury and the Importance of Respectful Curiosity in Supporting Youth Who Engage in Self-Injury

2019· article· en· W2991119709 on OpenAlexaff
Elizabeth E. Lloyd‐Richardson, Penelope Hasking, Stephen P. Lewis, Chloe A. Hamza, Margaret McAllister, Imke Baetens, Jennifer J. Muehlenkamp

Bibliographic record

VenueNASN School Nurse · 2019
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of TorontoUniversity of Guelph
Fundersnot available
KeywordsCuriosityPsychologySelf-destructive behaviorMental healthSuicide preventionPoison controlHuman factors and ergonomicsInjury preventionOccupational safety and healthNursingMedicineClinical psychologyPsychotherapistSocial psychologyMedical emergency

Abstract

fetched live from OpenAlex

Nonsuicidal self-injury (NSSI) is defined as the deliberate, self-inflicted damage of body tissue without suicidal intent and for purposes not socially or culturally sanctioned. School nurses are often a first point of contact for young people experiencing mental health challenges, and yet they often report they lack knowledge and training to provide care for persons who engage in NSSI. In the first of two parts, this article provides school nurses with a better understanding of NSSI and the distinctions between NSSI and suicidal behaviors, discusses the role of nurses' knowledge and attitudes on their ability to care for their patients' mental health needs, and discusses approaches for developing a respectful, empathic manner for working with and supporting youth who engage in self-injury. Part 2 will offer a strategy for brief assessment of NSSI and reflect on two case studies and their implications for school nursing practice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.346
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2019
Admission routes1
Has abstractyes

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