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Record W2929545979 · doi:10.1002/jocc.12115

Addressing Self‐Injury on College Campuses: Institutional Recommendations

2019· article· en· W2929545979 on OpenAlexaff
Stephen P. Lewis, Nancy L. Heath, Penelope Hasking, Janis Whitlock, Mark S. Wilson, Paul L. Plener

Bibliographic record

VenueJournal of College Counseling · 2019
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill UniversityUniversity of Guelph
Fundersnot available
KeywordsPsychologyMental healthMedical educationSelf-destructive behaviorResidenceApplied psychologyHuman factors and ergonomicsPoison controlMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

Nonsuicidal self‐injury is a significant concern on college campuses. Hence, the authors, the International Consortium on Self‐Injury in Educational Settings, offer the current position paper. First, the authors review current research in the field. Next, they discuss considerations for colleges' institutional‐wide response to self‐injury, including the role of and recommendations for relevant stakeholders (students, residence staff, mental health professionals) who directly and indirectly support students who self‐injure. Guidelines for response and suggested resources are also presented.

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)0.016
metaresearch head score (Gemma)0.050
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0060.003
Scholarly communication0.0060.006
Open science0.0050.009
Research integrity0.0190.012
Insufficient payload (model declined to judge)0.0240.005

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 head0.355
Teacher spread0.301 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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