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Record W2990633442 · doi:10.1177/1942602x19887353

Addressing Self-Injury in Schools, Part 2: How School Nurses Can Help With Supporting Assessment, Ongoing Care, and Referral for Treatment

2019· article· en· W2990633442 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
KeywordsReferralMedicineNursingSuicide preventionPoison controlMental healthInjury preventionHuman factors and ergonomicsPsychologyOccupational safety and healthPsychiatryMedical 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 second of two parts, this article offers a strategy for brief assessment of NSSI, as well as reflection on two case studies and how to offer support, ongoing care, and referral for treatment to youth who engage in self-injury.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0050.006
Open science0.0030.008
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.048
GPT teacher head0.384
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations8
Published2019
Admission routes1
Has abstractyes

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