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Record W2511787937 · doi:10.1111/jcap.12148

Suicide Risk Screening Tools and the Youth Population

2016· review· en· W2511787937 on OpenAlexaff
Sharon Patterson

Bibliographic record

VenueJournal of Child and Adolescent Psychiatric Nursing · 2016
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsHumber River Regional Hospital
Fundersnot available
KeywordsRisk assessmentSuicide preventionPopulationMedicinePoison controlOccupational safety and healthHealth careHuman factors and ergonomicsInjury preventionMedical emergencyPsychiatryPsychologyEnvironmental healthComputer securityComputer science

Abstract

fetched live from OpenAlex

TOPIC: The use of suicide risk screening tools is a critical component of a comprehensive approach to suicide risk assessment. Since nurses frequently spend more time with patients than any other healthcare professional, they are in key positions to detect and prevent suicidal behavior in youth. PURPOSE: To inform nurses about suicide risk screening tools for the youth population. Suicide risk screening tools are research-based standardized instruments that are used to identify people who may be at risk for suicide. SOURCES USED: A literature search was performed using the Athabasca University Library Resource, the databases of the Cumulative Index to Nursing and Allied Health Literature, ScienceDirect, and Google Scholar. CONCLUSIONS: Nurses are cautioned to utilize suicide risk screening tools as only part of the suicide risk assessment in youth populations and avoid the danger of relying on tools that may result in a blind application of evidence to the detriment of clinical experience and judgement.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0000.000

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 head0.047
GPT teacher head0.347
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations5
Published2016
Admission routes1
Has abstractyes

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