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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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
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 teacher head, not a consensus.

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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