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Record W2160195003 · doi:10.1080/13811118.2013.824829

The SAD PERSONS Scale for Suicide Risk Assessment: A Systematic Review

2014· review· en· W2160195003 on OpenAlexafffund
Sarah Warden, Rae Spiwak, Jitender Sareen, Shay‐Lee Bolton

Bibliographic record

VenueArchives of Suicide Research · 2014
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsPoison controlScale (ratio)Human factors and ergonomicsInjury preventionSuicide preventionClinical psychologyPsychologyMEDLINESystematic reviewMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

The SAD PERSONS scale (SPS) is widely used for suicide risk assessment in clinical and educational settings. The study objective was to systematically review the SPS performance in clinical situations. A systematic search of electronic databases was conducted. Relevant descriptive, quality, and outcome data were reviewed. In the search, 149 studies were identified and 9 met inclusion criteria. Included studies were highly variable across outcome measures, populations, and assessment methods. Only 3 studies examined SPS performance in predicting suicide outcomes; none showed the scale accurately predicted suicidal behavior. Available literature is of limited quality and quantity. Insufficient evidence exists to support SPS use in assessment or prediction of suicidal behavior. Well-designed studies that address the observed limitations are required.

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)0.006
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0120.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.168
GPT teacher head0.507
Teacher spread0.339 · 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 designSystematic review
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

Citations43
Published2014
Admission routes2
Has abstractyes

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