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Record W4300962567 · doi:10.1002/pits.22811

Slipping through the cracks: The critical role of school principals in addressing and responding to nonsuicidal self‐injury among adolescents

2022· article· en· W4300962567 on OpenAlexaff
Natalie M. Perkins, Melissa De Riggi, Penelope Hasking, Nancy L. Heath

Bibliographic record

VenuePsychology in the Schools · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill University
FundersCurtin University of Technology
KeywordsPsychologyPerceptionHuman factors and ergonomicsSuicide preventionPoison controlMedical educationClinical psychologyApplied psychologyMedicine

Abstract

fetched live from OpenAlex

Abstract Objective The responsibility of implementing nonsuicidal self‐injury (NSSI) policy falls largely on school principals, yet few have received training regarding adolescent NSSI. Understanding principals' perceptions and roles in responding to NSSI among their students is essential to determining how best to address and reduce NSSI within school settings. Method Principals and deputy principals ( n = 63) completed self‐report questionnaires. Interviews were also conducted with 24 respondents. Results Most principals were involved in the response to student reports of NSSI; however, few had received any training on appropriate responses to NSSI. Barriers to responding effectively were a lack of training as well as resources in the community and school. Conclusion Schools may benefit from more extensive and direct training about appropriately addressing and responding to NSSI within the school setting and an NSSI‐specific policy with clearly outlined roles for principals and other school staff.

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)0.031
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.062
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0060.004
Open science0.0020.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.055
GPT teacher head0.406
Teacher spread0.350 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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